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Provides a broad overview of computing at an introductory level, including topics such as security, robotics, and artificial intelligence. Increases understanding of how computers work and how algorithms solve problems. Design and implement creative applications in an introductory coding environment. Provides a broad overview of computing and algorithms without an emphasis on programming. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%2096" title="MATH 96" class="bubblelink code" onclick="return showCourse(this, 'MATH 96');">MATH 96</a> or placement into <a href="/search/?P=MATH%20141" title="MATH 141" class="bubblelink code" onclick="return showCourse(this, 'MATH 141');">MATH 141</a>. <a href="/search/?P=MATH%20118" title="MATH 118" class="bubblelink code" onclick="return showCourse(this, 'MATH 118');">MATH 118</a> does not fulfill the prerequisite. Not open to students with credit for <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a> or <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">320</a>Course Designation: Gen Ed - Quantitative Reasoning Part A Breadth - Natural Science Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Develop a fundamental understanding of the key concepts of computer science in a variety of contexts. Audience: Undergraduate 2. Create art, music, stories, games and other programs in a visual, introductory programming language Audience: Undergraduate 3. Understand how computers use algorithms to solve problems and act in intelligent ways. Audience: Undergraduate 4. Understand how computers utilize large sets of data to provide insight and knowledge. Audience: Undergraduate 5. Learn how software and hardware make modern computers work Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> COMP SCI 200 — PROGRAMMING I 3 credits. Learn the process of incrementally developing small (200-500 lines) programs along with the fundamental Computer Science topics. These topics include: problem abstraction and decomposition, the edit-compile-run cycle, using variables of primitive and more complex data types, conditional and loop-based flow control, basic testing and debugging techniques, how to define and call functions (methods), and IO processing techniques. Also teaches and reinforces good programming practices including the use of a consistent style, and meaningful documentation. Intended for students who have no prior programming experience. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied Quantitative Reasoning (QR) A requirement or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Design and implement a standalone program that can interact with the user via prompts and or menus, access and edit data stored in an array or list structure, and use and further process the data found in those structures. Audience: Undergraduate 2. Trace code to determine output or results. Audience: Undergraduate 3. Implement a given program design and choose correct control structures for implementing algorithms expressed in pseudocode. Audience: Undergraduate 4. Interpret a variety of diagram types used to express programming concepts and results: truth tables, memory model diagrams, control flow charts (activity diagrams), class diagrams, object diagrams, and use-case diagrams. Audience: Undergraduate 5. List, describe, use the basic I/O operations for reading and writing text files to and from the computer’s hard drive. Audience: Undergraduate 6. Manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information. Audience: Undergraduate 7. Evaluate models and arguments using quantitative information. Audience: Undergraduate 8. Express and interpret in context models, solutions and/or arguments using verbal, numerical, graphical algorithmic, computational or symbolic techniques. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> COMP SCI 220 — DATA SCIENCE PROGRAMMING I 4 credits. Introduction to Data Science programming using Python. No previous programming experience required. Emphasis on analyzing real datasets in a variety of forms and visual communication. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied Quantitative Reasoning (QR) A requirement or declared in the Professional Capstone Program in Computer Sciences. Not open to students with credit for COMP SCI 301.Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems Audience: Undergraduate 2. Demonstrate competencies with tools and processes necessary for data management and reproducibility Audience: Undergraduate 3. Produce meaning from data employing modeling strategies Audience: Undergraduate 4. Demonstrate critical thinking related to data science concepts and methods Audience: Undergraduate 5. Demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate 6. Manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH 240 — INTRODUCTION TO DISCRETE MATHEMATICS 3 credits. Basic concepts of logic, sets, partial order and other relations, and functions. Basic concepts of mathematics (definitions, proofs, sets, functions, and relations) with a focus on discrete structures: integers, bits, strings, trees, and graphs. Propositional logic, Boolean algebra, and predicate logic. Mathematical induction and recursion. Invariants and algorithmic correctness. Recurrences and asymptotic growth analysis. Fundamentals of counting. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20217" title="MATH 217" class="bubblelink code" onclick="return showCourse(this, 'MATH 217');">MATH 217</a> or <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a>Course Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 252 — INTRODUCTION TO COMPUTER ENGINEERING 3 credits. Logic components built with transistors, rudimentary Boolean algebra, basic combinational logic design, basic synchronous sequential logic design, basic computer organization and design, introductory machine- and assembly-language programming. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: NoneCourse Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Perform basic operations on binary representations for data Audience: Undergraduate 2. Analyze simple combinational and sequential digital logic and memory systems Audience: Undergraduate 3. Identify the components and operation of an instruction set processor and write programs using assembly language Audience: Undergraduate 4. Recognize and analyze ethical and professional responsibilities in engineering contexts Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 270 — FUNDAMENTALS OF HUMAN-COMPUTER INTERACTION 3 credits. User-centered software design including principles and methods for understanding user needs, designing and prototyping interface solutions, and evaluating their usability covered through lectures and hands-on in-class activities. Meets with <a href="/search/?P=COMP%20SCI%20570" title="COMP SCI 570" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 570');">COMP SCI 570</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Not open to students with credit for <a href="/search/?P=COMP%20SCI%20570" title="COMP SCI 570" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 570');">COMP SCI 570</a>Course Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2018</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 272 — INTRODUCTION TO WEB DEVELOPMENT 3 credits. Introduces methods and tools for creating/maintaining secure and interactive web content. Topics include programming fundamentals to support core web concepts, application development essentials, and content management systems. Web best practices - such as accessibility, design, and critical thinking about relevant ethics and organization. Covers practical skills to design and implement websites using popular scripting languages and frameworks, content management systems (CMSs), and related tools. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Not open to students with credit for <a href="/search/?P=L%20I%20S%20472" title="L I S/COMP SCI 472" class="bubblelink code" onclick="return showCourse(this, 'L I S 472');">L I S/COMP SCI 472</a>.Course Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Develop understanding and application of current web scripting languages and development tools and frameworks. Audience: Undergraduate 2. Install, configure, and customize open source content management systems. Audience: Undergraduate 3. Understand and apply user experience and accessibility best practices in building accessible web-based systems. Audience: Undergraduate 4. Design solutions to problems using multi-step scripting, logical operations, and functions. Audience: Undergraduate 5. Understand ethical issues and concerns related to website development and its related technologies. Audience: Undergraduate 6. Analyze the management challenges, and ethical considerations inherent in web development projects. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 298 — DIRECTED STUDY IN COMPUTER SCIENCE 1-3 credits. Undergraduate directed study in computer sciences. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> COMP SCI 300 — PROGRAMMING II 3 credits. Introduction to Object-Oriented Programming using classes and objects to solve more complex problems. Introduces array-based and linked data structures: including lists, stacks, and queues. Programming assignments require writing and developing multi-class (file) programs using interfaces, generics, and exception handling to solve challenging real world problems. Topics reviewed include reading/writing data and objects from/to files and exception handling, and command line arguments. Topics introduced: object-oriented design; class vs. object; create and define interfaces and iterators; searching and sorting; abstract data types (List,Stack,Queue,PriorityQueue(Heap),Binary Search Tree); generic interfaces (parametric polymorphism); how to design and write test methods and classes; array based vs. linked node implementations; introduction to complexity analysis; recursion. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied QR-A and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, 301, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>) or (<a href="/search/?P=E%20C%20E%20252" title="E C E/COMP SCI 252" class="bubblelink code" onclick="return showCourse(this, 'E C E 252');">E C E/COMP SCI 252</a> and <a href="/search/?P=E%20C%20E%20203" title="E C E 203" class="bubblelink code" onclick="return showCourse(this, 'E C E 203');">E C E 203</a>); graduate/professional standing; declared in Capstone Certificate in COMP SCI. Not open to students with credit for COMP SCI 367.Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. List and describe common operations for List, Stack, Queue, Priority Queue, Tree. Audience: Undergraduate 2. Analyze the time-complexity and compare the Big-Oh O(n) worse case complexity of different ADT implementations, and the complexity for any data structures and algorithms used to implement those operations. Audience: Undergraduate 3. Identify and properly test all boundary conditions for comprehensive testing of their programs. Audience: Undergraduate 4. Interpret and create a variety of diagrams: Call Stack trace; Stack, Queue, and Tree Data Structures; Recursive call (list or tree), control flow charts, class diagrams, object diagrams, and use-case diagrams. Audience: Undergraduate 5. Implement Object-Oriented (multi-class) standalone programs that manage a variety of data storage and retrieval operations (Program development skills and experience). Audience: Undergraduate 6. Evaluate models and arguments using quantitative information. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 304 — WES-CS GROUP MEETING 0-1 credits. Small group meetings for Wisconsin Emerging Scholars - Computer Science (WES-CS) students. Meets in small groups to work together on problems related to the <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a> course material. For information about WES-CS membership, contact the computer sciences department. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Concurrent enrollment in <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a> and member of Wisconsin Emerging Scholars program in Computer ScienceCourse Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 310 — PROBLEM SOLVING USING COMPUTERS 3 credits. Gives students an introduction to computer and analytical skills to use in their subsequent course work and professional development. Discusses several methods of using computers to solve problems, including elementary programming techniques, symbolic manipulation languages, and software packages. Techniques will be illustrated using sample problems drawn from elementary engineering. Emphasis is on introduction of algorithms with the use of specific tools to illustrate the methods. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20222" title="MATH 222" class="bubblelink code" onclick="return showCourse(this, 'MATH 222');">MATH 222</a>, graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Elementary L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 319 — DATA SCIENCE PROGRAMMING I FOR RESEARCH 3 credits. Introduction to Data Science programming using Python. In addition to a survey of programming basics (control flow and data structures), web scraping, database queries, and tabular analysis will be introduced. Projects will emphasize analyzing real datasets in a variety of forms and visual communication using plotting tools. Similar to <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">COMP SCI 220</a> but the pedagogical style of the projects will be adapted to graduate students in fields other than computer science and data science. No previous programming experience required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems Audience: Undergraduate 2. demonstrate competencies with tools and processes necessary for data management and reproducibility Audience: Undergraduate 3. produce meaning from data employing modeling strategies Audience: Undergraduate 4. demonstrate critical thinking related to data science concepts and methods Audience: Undergraduate 5. demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate 6. manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 320 — DATA SCIENCE PROGRAMMING II 4 credits. Intermediate approach to Data Science programming using Python. Experience with basic tabular analysis in Python is assumed. Learn to implement data structures (e.g., graphs) to efficiently represent datasets. Software-engineering tools such as version control and Python virtual environments will be introduced, with an emphasis on reproducibility of analysis. Tracing and A/B testing will be introduced as techniques for generating meaningful datasets. Introduces basic classification, clustering, optimization, and simulation techniques. Plotting and visual communication will be emphasized throughout the course. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">COMP SCI 220</a> (or COMP SCI 301 prior to Spring 2020), <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, <a href="/search/?P=COMP%20SCI%20319" title="COMP SCI 319" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 319');">319</a>, graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone CertificateCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems Audience: Undergraduate 2. demonstrate competencies with tools and processes necessary for data management and reproducibility Audience: Undergraduate 3. produce meaning from data employing modeling strategies Audience: Undergraduate 4. demonstrate critical thinking related to data science concepts and methods Audience: Undergraduate 5. conduct data science activities aware of and according to policy, privacy, security and ethical considerations Audience: Undergraduate 6. demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> COMP SCI/E C E 352 — DIGITAL SYSTEM FUNDAMENTALS 3 credits. Logic components, Boolean algebra, combinational logic analysis and synthesis, synchronous and asynchronous sequential logic analysis and design, digital subsystems, computer organization and design. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Satisfied Quantitative Reasoning (QR) A requirement and <a href="/search/?P=E%20C%20E%20252" title="E C E/COMP SCI 252" class="bubblelink code" onclick="return showCourse(this, 'E C E 252');">E C E/COMP SCI 252</a>Course Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Physical Sci. Counts toward the Natural Sci req Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock gradcap "> COMP SCI/E C E 354 — MACHINE ORGANIZATION AND PROGRAMMING 3 credits. An introduction to fundamental structures of computer systems and the C programming language with a focus on the low-level interrelationships and impacts on performance. Topics include the virtual address space and virtual memory, the heap and dynamic memory management, the memory hierarchy and caching, assembly language and the stack, communication and interrupts/signals, compiling and assemblers/linkers. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20252" title="E C E/COMP SCI 252" class="bubblelink code" onclick="return showCourse(this, 'E C E 252');">E C E/COMP SCI 252</a> and (<a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a> or 302) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Gen Ed - Quantitative Reasoning Part B Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 368 — LEARNING A PROGRAMMING LANGUAGE 1 credit. For students interested in learning a particular programming language. Focuses on a specific language offered at one of three levels: beginner, intermediate, and advanced. Students may repeat the course if the topic title is different. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: NoneCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 400 — PROGRAMMING III 3 credits. The third course in our programming fundamentals sequence. It presumes that students understand and use functional and object-oriented design and abstract data types as needed. This course introduces balanced search trees, graphs, graph traversal algorithms, hash tables and sets, and complexity analysis and about classes of problems that require each data type. Students are required to design and implement using high quality professional code, a medium sized program, that demonstrates knowledge and use of latest language features, tools, and conventions. Additional topics introduced will include as needed for projects: inheritance and polymorphism; anonymous inner classes, lambda functions, performance analysis to discover and optimize critical code blocks. Students learn about industry standards for code development. Students will design and implement a medium size project with a more advanced user-interface design, such as a web or mobile application with a GUI and event- driven implementation; use of version-control software. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 402 — INTRODUCING COMPUTER SCIENCE TO K-12 STUDENTS 2 credits. Work in teams to lead Computer Science clubs and workshops for K-12 students at sites in the Madison area. Design and lead activities to help K-12 students learn computational thinking and computer programming. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, 367, placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, or <a href="/search/?P=L%20I%20S%20102" title="L I S/COMP SCI 102" class="bubblelink code" onclick="return showCourse(this, 'L I S 102');">L I S/COMP SCI 102</a> (COMP SCI 202 prior to Fall 2023), graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Write programs in the Scratch programming language. Audience: Undergraduate 2. Design and lead after-school programming clubs for K-12 students. Audience: Undergraduate 3. Incorporate effective instructional strategies when teaching K-12 students. Audience: Undergraduate 4. Reflect on and document their teaching. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/STAT 403 — INTERNSHIP COURSE IN COMP SCI AND DATA SCIENCE 1 credit. Enables students with outside internships to earn academic credit connected to their work experience related to the Computer Sciences or Data Science programs. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, for 3 number of completionsLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand the challenges and opportunities in Computer Sciences and Data Science professions Audience: Undergraduate 2. Be prepared to find, apply and interview for a job and/or additional education Audience: Undergraduate 3. Articulate your career goals and long-term trajectory Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 407 — FOUNDATIONS OF MOBILE SYSTEMS AND APPLICATIONS 3 credits. Design and implementation of applications, systems, and services for mobile platforms with (i) constraints, such as limited processing, memory, energy, interfaces, variable bandwidth, and high mobility, and (ii) features, such as touchscreens, cameras, electronic compasses, GPS, and accelerometers. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a> or graduate/professional standingCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand the mobile ecosystem. Audience: Undergraduate 2. Understand basics of mobile system design and constituents, including wireless communication, location services, energy management, use of cloud services, and mobile device interface design. Audience: Undergraduate 3. Demonstrate the fundamentals of mobile app development: setting up the development environment, creating an app from scratch, and understanding the app lifecycle. Audience: Undergraduate 4. Understand the various app components, strategies for optimizing app performance, and use of mobile features. Audience: Undergraduate 5. Construct end-to-end applications that use server backends. Audience: Undergraduate 6. Demonstrate strong teamwork capabilities including collaborative working, task distribution, and conflict resolution. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 412 — INTRODUCTION TO NUMERICAL METHODS 3 credits. Interpolation, solution of linear and nonlinear systems of equations, approximate integration and differentiation, numerical solution of ordinary differential equations, Data fitting (such as least squares) by polynomials and splines. Knowledge of matrix algebra recommended, such as <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">MATH 340</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20222" title="MATH 222" class="bubblelink code" onclick="return showCourse(this, 'MATH 222');">MATH 222</a> and (<a href="/search/?P=MATH%20240" title="MATH/COMP SCI 240" class="bubblelink code" onclick="return showCourse(this, 'MATH 240');">MATH/COMP SCI 240</a> or <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a>) and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/MATH 425 — INTRODUCTION TO COMBINATORIAL OPTIMIZATION 3 credits. Focuses on optimization problems over discrete structures, such as shortest paths, spanning trees, flows, matchings, and the traveling salesman problem. We will investigate structural properties of these problems, and we will study both exact methods for their solution, and approximation algorithms. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify and use the structural properties of combinatorial optimization problems Audience: Undergraduate 2. Apply algorithms for the solution -exact or approximate- of a combinatorial optimization problem Audience: Undergraduate 3. Explain why the algorithms studied are correct and understand their running time Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/MATH 435 — INTRODUCTION TO CRYPTOGRAPHY 3 credits. Cryptography is the art and science of transmitting digital information in a secure manner. Provides an introduction to its technical aspects. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/STAT 471 — INTRODUCTION TO COMPUTATIONAL STATISTICS 3 credits. Classical statistical procedures arise where closed-form mathematical expressions are available for various inference summaries (e.g. linear regression; analysis of variance). A major emphasis of modern statistics is the development of inference principles in cases where both more complex data structures are involved and where more elaborate computations are required. Topics from numerical linear algebra, optimization, Monte Carlo (including Markov chain Monte Carlo), and graph theory are developed, especially as they relate to statistical inference (e.g., bootstrapping, permutation, Bayesian inference, EM algorithm, multivariate analysis). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20310" title="STAT/MATH 310" class="bubblelink code" onclick="return showCourse(this, 'STAT 310');">STAT/MATH 310</a> and (<a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">STAT 333</a> or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>), graduate/professional standing, or declared in Statistics VISPCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2020<div class="bubblehide">Learning Outcomes: 1. Use computational tools (alongside mathematical ones) to extract information from (a) the likelihood function, the central object of interest in frequentist statistics, and (b) the posterior distribution, the central object of interest in Bayesian statistics Audience: Undergraduate 2. Describe, understand the theoretical properties of, and implement basic algorithms for optimizing likelihood functions, including least squares and the IRLS algorithm, and the EM algorithm Audience: Undergraduate 3. Understand random numbers and pseudorandom numbers and how to distinguish them, and utilize a variety of techniques for generating random variates from a probability distribution Audience: Undergraduate 4. Use Monte Carlo methodology for such purposes as (a) carrying out a simulation study to study the properties of a statistical method, or (b) performing statistical inference via the bootstrap, or MCMC Audience: Undergraduate 5. Understand the use of graphical models for representing the structure of complex joint distributions, and be able to use computational tools to extract information from graphical models Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/L I S 472 — INTRODUCTION TO WEB DEVELOPMENT 3 credits. Applied web development introduces methods and tools for creating/maintaining secure and interactive web content. Topics include programming fundamentals to support core web concepts, application development essentials, and content management systems. Web best practices - such as accessibility, design, and critical thinking about relevant ethics and organization - will be incorporated throughout. Gain practical skills to design and implement websites using popular scripting languages and frameworks, content management systems (CMSs), and related tools. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Junior standing, declared in Library and Information Studies MA, Information MS, or Capstone Certificate in Computer Sciences for Professionals. Not open to students with credit for <a href="/search/?P=COMP%20SCI%20272" title="COMP SCI 272" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 272');">COMP SCI 272</a>.Course Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Develop understanding and application of current web scripting languages and development tools and frameworks. Audience: Both Grad & Undergrad 2. Install, configure, and customize open source content management systems. Audience: Both Grad & Undergrad 3. Understand and apply user experience and accessibility best practices in building accessible websites. Audience: Both Grad & Undergrad 4. Design solutions to problems using multi-step scripting, logical operations, and functions. Audience: Both Grad & Undergrad 5. Understand ethical issues and concerns related to website development and its related technologies. Audience: Both Grad & Undergrad 6. Analyze the management challenges, and ethical considerations inherent in web development projects. Audience: Both Grad & Undergrad 7. Critically evaluate and compare different frameworks and libraries for extending scripting capabilities. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH/STAT 475 — INTRODUCTION TO COMBINATORICS 3 credits. Problems of enumeration, distribution, and arrangement. Inclusion-exclusion principle. Generating functions and linear recurrence relations. Combinatorial identities. Graph coloring problems. Finite designs. Systems of distinct representatives and matching problems in graphs. Potential applications in the social, biological, and physical sciences. Puzzles. Problem solving. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand basic counting strategies, such as staged thought-experiments, inclusion/exclusion, generating functions, and recurrence relations, and apply these strategies to solve a wide variety of counting problems. Audience: Undergraduate 2. Recall basic objects that are used in combinatorics, such as permutations and combinations of sets and multisets, binomial and multinomial coefficients, the Catalan numbers, the Stirling numbers, and the partition numbers. Audience: Undergraduate 3. Analyze a given combinatorial problem using the standard theorems of combinatorics, such as the pigeonhole principle, the Newton binomial theorem, the multinomial theorem, the Ramsey theorem, the Dilworth theorem, the Burnside theorem, and the Polya counting theorem. Audience: Undergraduate 4. Construct mathematical arguments related to combinatorial problems using the above definitions, properties, theorems, and counting strategies; including the construction of examples and counterexamples. Audience: Undergraduate 5. Convey his or her arguments in oral and written form in English, using appropriate mathematical terminology, notation, and grammar. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/CURRIC 502 — THEORY AND PRACTICE IN COMPUTER SCIENCE EDUCATION 1 credit. Computer science educational pedagogy and general teaching practices. Practical experience gained through tutoring students. Knowledge of object-oriented programming required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a> or 302 or declared in Computer Science graduate programCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 506 — SOFTWARE ENGINEERING 3 credits. Ideas and techniques for designing, developing, and modifying large software systems. Topics include software engineering processes; requirements and specifications; project team organization and management; software architectures; design patterns; testing and debugging; and cost and quality metrics and estimation. Students will work in large teams on a substantial programming project. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) and (<a href="/search/?P=COMP%20SCI%20407" title="COMP SCI 407" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 407');">COMP SCI 407</a>, <a href="/search/?P=COMP%20SCI%20536" title="COMP SCI 536" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 536');">536</a>, <a href="/search/?P=COMP%20SCI%20537" title="COMP SCI 537" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 537');">537</a>, 545, <a href="/search/?P=COMP%20SCI%20559" title="COMP SCI 559" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 559');">559</a>, <a href="/search/?P=COMP%20SCI%20564" title="COMP SCI 564" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 564');">564</a>, <a href="/search/?P=COMP%20SCI%20570" title="COMP SCI 570" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 570');">570</a>, 679 or <a href="/search/?P=E%20C%20E%20552" title="E C E/COMP SCI 552" class="bubblelink code" onclick="return showCourse(this, 'E C E 552');">E C E/COMP SCI 552</a>) or graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH 513 — NUMERICAL LINEAR ALGEBRA 3 credits. Direct and iterative solution of linear and nonlinear systems and of eigenproblems. LU and symmetric LU factorization. Complexity, stability, and conditioning. Nonlinear systems. Iterative methods for linear systems. QR-factorization and least squares. Eigenproblems: local and global methods. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">MATH 340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>) or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) programCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH 514 — NUMERICAL ANALYSIS 3 credits. Polynomial forms, divided differences. Polynomial interpolation. Polynomial approximation: uniform approximation and Chebyshev polynomials, least-squares approximation and orthogonal polynomials. Numerical differentiation and integration. Splines, B-splines and spline approximation. Numerical methods for solving initial and boundary value problems for ordinary differential equations. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>), (<a href="/search/?P=MATH%20322" title="MATH 322" class="bubblelink code" onclick="return showCourse(this, 'MATH 322');">MATH 322</a>, <a href="/search/?P=MATH%20376" title="MATH 376" class="bubblelink code" onclick="return showCourse(this, 'MATH 376');">376</a>, <a href="/search/?P=MATH%20421" title="MATH 421" class="bubblelink code" onclick="return showCourse(this, 'MATH 421');">421</a>, or <a href="/search/?P=MATH%20521" title="MATH 521" class="bubblelink code" onclick="return showCourse(this, 'MATH 521');">521</a>), and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, or 301 prior to Spring 2020, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>); grad/professional standing; member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Recall and state the formal definitions of the mathematical objects and their properties used in numerical analysis (e.g., Lagrange polynomials, Gibbs phenomenon, Runge phenomenon, orthogonal polynomials, recurrence relation, Gaussian quadrature points, splines, etc.). Audience: Both Grad & Undergrad 2. Use different techniques of numerical analysis in their appropriate settings (e.g., polynomial interpolation, least square approximation, discrete Fourier transform, the Golub-Welsch algorithm, fast Fourier transform, trapezoidal rule and Simpson's rule, numerical differentiation, forward and backward Euler's method, etc.). Audience: Both Grad & Undergrad 3. State the main theoretical results related to the error analysis for different methods (e.g., least square error, numerical integration using a Riemann sum, the trapezoidal rule, Simpson's rule and Gaussian quadratures, (semi-)discrete Fourier transform, forward and backward Euler, etc.), and recall the arguments for these theorems and the underlying logic of their proofs. Audience: Both Grad & Undergrad 4. Convey arguments in oral and written forms using English and appropriate mathematical terminology, notation and grammar. Audience: Both Grad & Undergrad 5. Identify applications of course content in current areas of research. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/DS/I SY E 518 — WEARABLE TECHNOLOGY 3 credits. Gives students hands-on experience in building wearable computing platforms. Designed for students who have a background in textiles and apparel design, computer science, engineering or media arts. By the completion of the course students will have fundamental knowledge of electronic circuitry, programming, and "maker skills". <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Sophomore standingCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 520 — INTRODUCTION TO THEORY OF COMPUTING 3 credits. Basics about the notion, capabilities, and limitations of computation: elements of finite automata and regular languages, computability theory, and computational complexity theory. Additional topics include context-free grammars and languages, and complexity-theoretic cryptography. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20240" title="MATH/COMP SCI 240" class="bubblelink code" onclick="return showCourse(this, 'MATH 240');">MATH/COMP SCI 240</a> or <a href="/search/?P=STAT%20475" title="STAT/COMP SCI/MATH 475" class="bubblelink code" onclick="return showCourse(this, 'STAT 475');">STAT/COMP SCI/MATH 475</a>) and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>), or graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/I SY E 524 — INTRODUCTION TO OPTIMIZATION 3 credits. Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>) and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) or graduate/professional standingCourse Designation: Breadth - Natural Science Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Engage in topics about "optimization in practice". Audience: Undergraduate 2. Use and analyze the results of state of the art optimization software. Audience: Undergraduate 3. Use the GAMS modeling system and Jupyter notebooks (in conjunction with elementary Python) or Julia and JUMP. Audience: Undergraduate 4. Design good models for realistic applications in engineering and the sciences. Audience: Undergraduate 5. Develop a "commercial strength" application of optimization technology. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/MATH/STAT 525 — LINEAR OPTIMIZATION 3 credits. Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, or <a href="/search/?P=MATH%20443" title="MATH 443" class="bubblelink code" onclick="return showCourse(this, 'MATH 443');">443</a> or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Use linear programming to formulate real world decision problems. Audience: Both Grad & Undergrad 2. Apply algorithms to solve linear programming problems and demonstrate their correctness. Audience: Both Grad & Undergrad 3. Combine different proving techniques explored in class in an original way to show new results. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E 526 — ADVANCED LINEAR PROGRAMMING 3 credits. Review of linear programming. Polynomial time methods for linear programming. Quadratic programs and linear complementarity problems and related solution techniques. Solution sets and their continuity properties. Error bounds for linear inequalities and programs. Parallel algorithms for linear and quadratic programs. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20525" title="STAT/COMP SCI/I SY E/MATH 525" class="bubblelink code" onclick="return showCourse(this, 'STAT 525');">STAT/COMP SCI/I SY E/MATH 525</a> and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>) or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2020<div class="bubblehide">Learning Outcomes: 1. Use the theory of linear programming to prove general duality results Audience: Undergraduate 2. Apply the concept of complementarity Audience: Undergraduate 3. Analyze and develop algorithms for solving optimization and equilibrium problems Audience: Undergraduate 4. Apply decomposition methods and other advanced algorithms for the solution of optimization and equilibrium problems Audience: Undergraduate 5. Understand economic concepts and how they relate to optimization and equilibria Audience: Undergraduate 6. Extend theory of linear programming into the framework of conic programming Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/M E 532 — MATRIX METHODS IN MACHINE LEARNING 3 credits. Linear algebraic foundations of machine learning featuring real-world applications of matrix methods from classification and clustering to denoising and data analysis. Mathematical topics include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Previous exposure to numerical computing (e.g. Matlab, Python, Julia, R) required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a>, <a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) and (<a href="/search/?P=E%20C%20E%20203" title="E C E 203" class="bubblelink code" onclick="return showCourse(this, 'E C E 203');">E C E 203</a>, <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">320</a>, or placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Use matrices and vectors to formulate classification, prediction and matrix completion problems using techniques such as least squares, regularized least squares, the singular value decomposition, subspace methods, support vector machines, neural networks and kernel methods. Audience: Both Grad & Undergrad 2. Implement machine learning techniques for classification, prediction and matrix completion problems in software, and validate their performance on datasets using cross validation. Audience: Both Grad & Undergrad 3. Apply advanced techniques to formulate and prove optimality of various matrix based techniques in machine learning. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 533 — IMAGE PROCESSING 3 credits. Mathematical representation of continuous and digital images; models of image degradation; picture enhancement, restoration, segmentation, and coding; pattern recognition, tomography. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20330" title="E C E 330" class="bubblelink code" onclick="return showCourse(this, 'E C E 330');">E C E 330</a> and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a> or <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>), graduate/professional standing, or member of Engineering Guest StudentsCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 534 — COMPUTATIONAL PHOTOGRAPHY 3 credits. Study of sensing and computational techniques that enhance or extend the capabilities of digital photography by using methods from computer vision and computer graphics to create new visual representations. Algorithms for analyzing, improving, manipulating, combining, and synthesizing images. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a> or 367) and (<a href="/search/?P=MATH%20217" title="MATH 217" class="bubblelink code" onclick="return showCourse(this, 'MATH 217');">MATH 217</a> or <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2018</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 536 — INTRODUCTION TO PROGRAMMING LANGUAGES AND COMPILERS 3 credits. Introduction to the theory and practice of compiler design. Comparison of features of several programming languages and their implications for implementation techniques. Several programming projects required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20354" title="E C E/COMP SCI 354" class="bubblelink code" onclick="return showCourse(this, 'E C E 354');">E C E/COMP SCI 354</a> and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 537 — INTRODUCTION TO OPERATING SYSTEMS 4 credits. Input-output hardware, interrupt handling, properties of magnetic tapes, discs and drums, associative memories and virtual address translation techniques. Batch processing, time sharing and real-time systems, scheduling resource allocation, modular software systems, performance measurement and system evaluation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20354" title="E C E/COMP SCI 354" class="bubblelink code" onclick="return showCourse(this, 'E C E 354');">E C E/COMP SCI 354</a> and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 538 — INTRODUCTION TO THE THEORY AND DESIGN OF PROGRAMMING LANGUAGES 3 credits. Design and theory of programming languages: procedural, object-oriented, functional and logic paradigms. Serial and concurrent programming. Execution models and formal specification techniques. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20354" title="E C E/COMP SCI 354" class="bubblelink code" onclick="return showCourse(this, 'E C E 354');">E C E/COMP SCI 354</a> and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/M E 539 — INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 3 credits. Theory and applications of artificial neural networks: multi-layer perceptron, self-organization mapdeep neural network convolutional neural network, recurrent network, support vector machines genetic algorithm, and evolution computing. Applications to control, pattern recognition, prediction, and object detection and tracking. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify if a given data analysis task is a pattern classification problem or a model approximation problem. Audience: Undergraduate 2. Apply multi-layer perceptron neural network training algorithm to develop artificial neural network (ANN) based pattern classifiers and data predictors. Audience: Undergraduate 3. Apply deep learning network for pattern classification Audience: Undergraduate 4. Apply support vector machine (SVM) to develop pattern classifiers. Audience: Undergraduate 5. Apply self-organization map and k-means to perform clustering operations of a given data set. Audience: Undergraduate 6. Apply stochastic optimization methods, including simulated annealing, genetic algorithm and random search to solve a discrete optimization problem. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 540 — INTRODUCTION TO ARTIFICIAL INTELLIGENCE 3 credits. Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">320</a> or 367) and (<a href="/search/?P=MATH%20211" title="MATH 211" class="bubblelink code" onclick="return showCourse(this, 'MATH 211');">MATH 211</a>, <a href="/search/?P=MATH%20217" title="MATH 217" class="bubblelink code" onclick="return showCourse(this, 'MATH 217');">217</a>, <a href="/search/?P=MATH%20221" title="MATH 221" class="bubblelink code" onclick="return showCourse(this, 'MATH 221');">221</a>, or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. (Uninformed Search Methods) Identify the formulation of search for problem solving tasks. Understand important concepts in uninformed search. Apply the search methods on the formulated search problem. Audience: Undergraduate 2. (Informed Search Methods) Understand important concepts in informed search. Differentiate from uninformed search. Solve the formulated search problem with the informed search method A*. Audience: Undergraduate 3. (Local Search Methods) Identify the formulation of search for problem solving tasks. Apply the hill climbing method for local search problems. Identify and summarize the important features of the simulated annealing and genetic algorithms. Audience: Undergraduate 4. (Game Playing) Recall the concept of games. Perform the minimax game playing method on formulated game tasks. Apply alpha-beta pruning to speed up the minimax method. Audience: Undergraduate 5. (Unsupervised and Supervised Learning) Identify and summarize important features about supervised learning and unsupervised learning. Differentiate between the two types of tasks. Audience: Undergraduate 6. (Classic Learning Methods) Apply linear regression, hierarchical agglomerative clustering algorithm, k-means clustering, or K nearest neighbor algorithm on given problem instances. Judge if the method is appropriate for a given task. Audience: Undergraduate 7. (Neural Networks and Deep Learning) Apply Perceptron learning rule on given problem instances. Implement neural networks using given software packages. Audience: Undergraduate 8. (Reinforcement Learning) Understand the concepts of reinforcement learning. Identify and summarize its important features. Compute value function and Q function. Apply value iteration and Q learning on given problems. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 541 — THEORY & ALGORITHMS FOR DATA SCIENCE 3 credits. Theoretical methods for data science. Topics include: review of probability background, concentration inequalities, geometry of high dimensional random variables, parametric and non-parametric estimation, selected topics from optimization (optimality conditions; deterministic and stochastic gradient descent), PAC learning, sample complexity and algorithms for linear classification and regression, and property/distribution testing. Uses Python programming language. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20220" title="COMP SCI 220" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 220');">220</a>, or <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">STAT 340</a>), (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>), and (<a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>, <a href="/search/?P=STAT%20333" title="STAT 333" class="bubblelink code" onclick="return showCourse(this, 'STAT 333');">333</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, <a href="/search/?P=MATH%20309" title="MATH/STAT 309" class="bubblelink code" onclick="return showCourse(this, 'MATH 309');">MATH/STAT 309</a>, <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">431</a>, <a href="/search/?P=MATH%20331" title="MATH 331" class="bubblelink code" onclick="return showCourse(this, 'MATH 331');">MATH 331</a>, <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">531</a>, or <a href="/search/?P=I%20SY%20E%20210" title="I SY E 210" class="bubblelink code" onclick="return showCourse(this, 'I SY E 210');">I SY E 210</a>), or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Use and explain theoretical concepts in data science, such as concentration inequalities, learning, nonparametric distribution testing, optimization. Audience: Undergraduate 2. Use properties of high-dimensional random variables to perform tasks such as dimension reduction and separating mixtures of Gaussians Audience: Undergraduate 3. Use optimization algorithms such as gradient descent and stochastic gradient descent and analyze their computational complexity Audience: Undergraduate 4. Analyze sample complexity for basic learning tasks such binary linear classification, as well as computational complexity of algorithms for solving them Audience: Undergraduate 5. Analyze sample complexity of distribution testing tasks, such as testing uniformity and testing equivalence of discrete distributions Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 542 — INTRODUCTION TO SOFTWARE SECURITY 3 credits. Teaches the security considerations that occur during all steps of the software development life cycle: methodologies for designing secure software, programming using secure programming techniques, in-depth vulnerability assessment methodologies, static and dynamic analysis tools for evaluating software security, and system defenses reducing security threats. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a> or <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">320</a>, graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Think like an attacker, that is to think about software the way that an adversary would. Audience: Undergraduate 2. Design secure programs based on a structured methodology such as Threat Modeling. Have security in mind before writing the first line of code. Audience: Undergraduate 3. Program in a secure way by mastering a comprehensive set of techniques for producing code that is more resilient to attack. Audience: Undergraduate 4. Think like a security analyst, that is how to perform an in-depth software vulnerability assessments using a structured methodology such as First Principles Vulnerability Assessment (FPVA). Acquire the skills of a security analyst. Audience: Undergraduate 5. Use a variety of automated tools that help statically and dynamically analyze code for security flaws. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 544 — INTRODUCTION TO BIG DATA SYSTEMS 3 credits. Deploy and use distributed systems to store and analyze large datasets. Unstructured and structured approaches to storage will be covered. Analysis will involve learning new query languages, processing streaming data, and training machine learning models. Most programming will be done in Python. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">COMP SCI 320</a>, <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>, or Graduate/Professional StandingCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Deploy distributed systems for data storage and analytics Audience: Undergraduate 2. Demonstrate competencies with tools and processes necessary for loading data into distributed storage systems Audience: Undergraduate 3. Write programs that use distributed platforms to efficiently analyze large datasets Audience: Undergraduate 4. Produce meaning from large datasets by training machine learning models in parallel or on distributed systems Audience: Undergraduate 5. Measure resource usage and overall cost of running distributed programs Audience: Undergraduate 6. Optimize distributed analytics programs to reduce resource consumption and program runtime Audience: Undergraduate 7. Demonstrate competencies with cloud services designed to store or analyze large datasets Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 552 — INTRODUCTION TO COMPUTER ARCHITECTURE 3 credits. The design of computer systems and components. Processor design, instruction set design, and addressing; control structures and microprogramming; memory management, caches, and memory hierarchies; and interrupts and I/O structures. <a href="/search/?P=E%20C%20E%20551" title="E C E 551" class="bubblelink code" onclick="return showCourse(this, 'E C E 551');">E C E 551</a> or knowledge of Verilog is recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=E%20C%20E%20352" title="E C E/COMP SCI 352" class="bubblelink code" onclick="return showCourse(this, 'E C E 352');">E C E/COMP SCI 352</a> and <a href="/search/?P=E%20C%20E%20354" title="E C E/COMP SCI 354" class="bubblelink code" onclick="return showCourse(this, 'E C E 354');">E C E/COMP SCI 354</a>) or graduate/professional standingCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/M E 558 — INTRODUCTION TO COMPUTATIONAL GEOMETRY 3 credits. Introduction to fundamental geometric computations and algorithms, and their use for solving engineering and scientific problems. Computer representations of simple geometric objects and paradigms for algorithm design. Applications from areas of engineering analysis, design and manufacturing, biology, statistics, and other sciences. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) and <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a> or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2018</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 559 — COMPUTER GRAPHICS 3 credits. Survey of computer graphics. Image representation, formation, presentation, composition and manipulation. Modeling, transformation, and display of geometric objects in two and three dimensions. Representation of curves and surfaces. Rendering, animation, multi-media and visualization. Fluency with vector mathematics (e.g., from <a href="/search/?P=MATH%20234" title="MATH 234" class="bubblelink code" onclick="return showCourse(this, 'MATH 234');">MATH 234</a> or a linear algebra class) is recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=MATH%20222" title="MATH 222" class="bubblelink code" onclick="return showCourse(this, 'MATH 222');">MATH 222</a> and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 561 — PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING 3 credits. Probabilistic tools for machine learning and analysis of real-world datasets. Introductory topics include classification, regression, probability theory, decision theory and quantifying information with entropy, relative entropy and mutual information. Additional topics include naive Bayes, probabilistic graphical models, discriminant analysis, logistic regression, expectation maximization, source coding and variational inference. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>, or <a href="/search/?P=M%20E%20532" title="M E/COMP SCI/E C E 532" class="bubblelink code" onclick="return showCourse(this, 'M E 532');">M E/COMP SCI/E C E 532</a> or concurrent enrollment) and (<a href="/search/?P=E%20C%20E%20331" title="E C E 331" class="bubblelink code" onclick="return showCourse(this, 'E C E 331');">E C E 331</a>, <a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">STAT/MATH 309</a>, <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">431</a>, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">STAT 311</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, <a href="/search/?P=M%20E%20424" title="M E/STAT 424" class="bubblelink code" onclick="return showCourse(this, 'M E 424');">M E/STAT 424</a> or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">MATH 531</a>) or grad/profsnl standing or declared in Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify how ambiguity and noise leads to the need for probabilistic methods in machine learning Audience: Both Grad & Undergrad 2. Implement classification, prediction and generative algorithms using a variety of techniques based in probability, information theory and machine learning Audience: Both Grad & Undergrad 3. Prove optimality of a variety of algorithms and demonstrate understanding of sample complexity bounds Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 564 — DATABASE MANAGEMENT SYSTEMS: DESIGN AND IMPLEMENTATION 4 credits. What a database management system is; different data models currently used to structure the logical view of the database: relational, hierarchical, and network. Hands-on experience with relational and network-based database systems. Implementation techniques for database systems. File organization, query processing, concurrency control, rollback and recovery, integrity and consistency, and view implementation. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20354" title="E C E/COMP SCI 354" class="bubblelink code" onclick="return showCourse(this, 'E C E 354');">E C E/COMP SCI 354</a> and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 565 — INTRODUCTION TO DATA VISUALIZATION 3 credits. Introduction to topics such as perception, cognition, communication, design, implementation, applications, tools, and evaluation. Provides a broad survey of the field and covers fundamental concepts, theory, and tools in data visualization with opportunities for hands-on activities. Gain real-world experience in designing and evaluating visualizations. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">COMP SCI 320</a>, <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>, or Graduate/Professional StandingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Differentiate between visualizations to communicate within situational context. Audience: Undergraduate 2. Illustrate the types of properties that make visualizations more effective. Audience: Undergraduate 3. Apply techniques and algorithms for creating effective visualizations based on principles and techniques from graphic design, visual art, perceptual psychology, and cognitive science. Audience: Undergraduate 4. Utilize visualization creation tools in individual and group assignment Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 566 — INTRODUCTION TO COMPUTER VISION 3 credits. Topics include image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. Broad overview of various computer vision and machine learning techniques and sensing and imaging technologies used in computer vision applications. This is a project-based course. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a> and (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a> or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) and (STAT 302, <a href="/search/?P=STAT%20311" title="STAT 311" class="bubblelink code" onclick="return showCourse(this, 'STAT 311');">311</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">324</a>, <a href="/search/?P=STAT%20340" title="STAT 340" class="bubblelink code" onclick="return showCourse(this, 'STAT 340');">340</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>, <a href="/search/?P=STAT%20309" title="STAT/MATH 309" class="bubblelink code" onclick="return showCourse(this, 'STAT 309');">STAT/MATH 309</a>, <a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">431</a>, <a href="/search/?P=MATH%20331" title="MATH 331" class="bubblelink code" onclick="return showCourse(this, 'MATH 331');">MATH 331</a> or <a href="/search/?P=MATH%20531" title="MATH 531" class="bubblelink code" onclick="return showCourse(this, 'MATH 531');">531</a>) or graduate/professional standingCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Know a broad range of fundamental concepts in 2D and 3D computer vision, including basic image processing, 2D image recognition, 3D sensing and motion recovery. Audience: Undergraduate 2. Demonstrate an understanding of basic theories, current approaches, key concepts, and common practices in computer vision. Audience: Undergraduate 3. Design basic statistics and machine learning algorithms for computer vision problems, conduct experiments to evaluate the proposed approaches, and analyze and interpret the results. Audience: Undergraduate 4. Develop basic computer vision applications and projects in MATLAB. Audience: Undergraduate 5. Communicate effectively through written reports, oral presentations, and discussions. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 567 — MEDICAL IMAGE ANALYSIS 3 credits. Present introductory medical image processing and analysis techniques. Topics include medical imaging formats, segmentation, registration, image quantification, classification. Strongly encourage Matlab experience, such as <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">COMP SCI 310</a> or <a href="/search/?P=COMP%20SCI%20368" title="COMP SCI 368" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 368');">368</a>-Matlab. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a> or <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>) and (STAT 511, <a href="/search/?P=STAT%20541" title="STAT/B M I 541" class="bubblelink code" onclick="return showCourse(this, 'STAT 541');">541</a>, <a href="/search/?P=POP%20HLTH%20551" title="POP HLTH/B M I 551" class="bubblelink code" onclick="return showCourse(this, 'POP HLTH 551');">POP HLTH/B M I 551</a>, <a href="/search/?P=STAT%20324" title="STAT 324" class="bubblelink code" onclick="return showCourse(this, 'STAT 324');">STAT 324</a>, <a href="/search/?P=STAT%20371" title="STAT 371" class="bubblelink code" onclick="return showCourse(this, 'STAT 371');">371</a>, or <a href="/search/?P=STAT%20571" title="STAT/F&W ECOL 571" class="bubblelink code" onclick="return showCourse(this, 'STAT 571');">STAT/F&W ECOL 571</a>) or graduate/professional standingCourse Designation: Breadth - Biological Sci. Counts toward the Natural Sci req Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Implement the key principles of ideas from probability, statistics and computer vision algorithms used in medical image analysis Audience: Undergraduate 2. Recognize which image analysis problems will benefit from which modeling approach Audience: Undergraduate 3. Apply algorithms about image analysis tasks and implement algorithms and pipelines using MATLAB Audience: Undergraduate 4. Implement the building blocks taught in this course to independently learn and apply new, but related imaging analysis algorithms Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 570 — INTRODUCTION TO HUMAN-COMPUTER INTERACTION 3 credits. User-centered software design: (1) principles of and methods for understanding user needs; designing and prototyping interface solutions; and evaluating their usability, (2) their applications in designing multiple types of interfaces through group projects. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Junior standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand fundamental concepts and principles of Human-Computer Interaction (HCI). Audience: Undergraduate 2. Apply user-centered design principles and methods to create effective and usable interfaces. Audience: Undergraduate 3. Analyze and evaluate user interfaces using appropriate usability evaluation techniques. Audience: Undergraduate 4. Develop a toolbox of user-centered design techniques that can be applied to new design problems. Audience: Undergraduate 5. Understand user needs and expectations with the aid of user research methods. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 571 — BUILDING USER INTERFACES 3 credits. Introduces software development of user interfaces (UIs). Build competence in implementing UIs using state-of-the-art (1) UI paradigms, such as event-driven interfaces, direct-manipulation interfaces, and dialogue-based interaction; (2) methods for capturing, interpreting, and responding to different forms of user input and states, including pointing, text entry, speech, touch, gestures, user activity, context, and physiological states; and (3) platform-specific UI development APIs, frameworks, and toolkits for multiple platforms including web/mobile/desktop interfaces, natural user interfaces, and voice user interfaces. Learn about the fundamental concepts, technologies, algorithms, and methods in building user interfaces, implement UIs using of state-of-the-art UI development tools, and build a UI development portfolio. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a>Course Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Engage in design thinking around user interface needs and problems, ideate and communicate conceptual design solutions Audience: Undergraduate 2. Create visual designs, layouts, and navigation structures, and effectively use design languages, color palettes, and platform-specific design elements Audience: Undergraduate 3. Prototype and develop user interfaces for the Web, mobile, and voice user interfaces (VUIs) Audience: Undergraduate 4. Program front-end, user-facing software elements using the state-of-the-art programming languages, frameworks, and libraries Audience: Undergraduate 5. Follow user-centered design principles, heuristics, and methods to iteratively build, assess, and refine design solutions Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 576 — INTRODUCTION TO BIOINFORMATICS 3 credits. Algorithms for computational problems in molecular biology. Studies algorithms for problems such as: genome sequencing and mapping, pairwise and multiple sequence alignment, modeling sequence classes and features, phylogenetic tree construction, and gene-expression data analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">COMP SCI 320</a> or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>) and <a href="/search/?P=MATH%20222" title="MATH 222" class="bubblelink code" onclick="return showCourse(this, 'MATH 222');">MATH 222</a>, graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Explain the biology and significance of the most commonly measured molecules in molecular biology. Audience: Undergraduate 2. Identify the primary computational problems associated with each type of biological data. Audience: Undergraduate 3. Explain the major algorithms and approaches used to address the computational problems. Audience: Undergraduate 4. Implement efficient algorithms for bioinformatic tasks through the use of the discussed approaches. Audience: Undergraduate 5. Apply the discussed algorithms to novel but closely-related tasks. Audience: Undergraduate 6. Understand the methods covered such that parts of the methods sections of published biological papers are interpretable. Audience: Undergraduate 7. Begin to gain the qualifications of a bioinformatician. Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 577 — INTRODUCTION TO ALGORITHMS 4 credits. Basic paradigms for the design and analysis of efficient algorithms: greed, divide-and-conquer, dynamic programming, reductions, and the use of randomness. Computational intractability including typical NP-complete problems and ways to deal with them. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20240" title="MATH/COMP SCI 240" class="bubblelink code" onclick="return showCourse(this, 'MATH 240');">MATH/COMP SCI 240</a> or <a href="/search/?P=STAT%20475" title="STAT/COMP SCI/MATH 475" class="bubblelink code" onclick="return showCourse(this, 'STAT 475');">STAT/COMP SCI/MATH 475</a>) and (COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">400</a>), or graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 578 — CONTEST-LEVEL PROGRAMMING 1 credit. Training in computer programming for competitions: assessing the coding difficulty and complexity of computational problems, recognizing the applicability of known algorithms, fast coding and testing, team work. <a href="/search/?P=COMP%20SCI%20577" title="COMP SCI 577" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 577');">COMP SCI 577</a> is suggested but not required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a> or 367), graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2020</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/DS 579 — VIRTUAL REALITY 3 credits. Introduces students to the field of virtual reality and focuses on creating immersive, interactive virtual experiences. Survey topics include historical perspectives on virtual reality technology, computer graphics and 3D modeling, human perception and psychology, human computer interaction and user interface design. This course is designed for students with backgrounds in Computer Science, Engineering, Art, Architecture and Design. Students will work in interdisciplinary teams on projects, culminating in a final event that will be showcased to the public. While not an official uisite, the class will be technologically motivated; therefore students should be comfortable learning new software. The class will utilize publicly available game design software which provides tools and services for the creation of interactive content. While not necessary, students may find it helpful to have taken classes in programming and computer graphics (such <a href="/search/?P=COMP%20SCI%20559" title="COMP SCI 559" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 559');">COMP SCI 559</a>: Computer Graphics) or in 3D modeling (such as <a href="/search/?P=ART%20429" title="ART 429" class="bubblelink code" onclick="return showCourse(this, 'ART 429');">ART 429</a>: 3D Digital Studio I or <a href="/search/?P=DS%20242" title="DS 242" class="bubblelink code" onclick="return showCourse(this, 'DS 242');">DS 242</a>: Visual Communication II). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Sophomore standingCourse Designation: Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Summer 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/L I S 611 — USER EXPERIENCE DESIGN 1 3 credits. Introduction to the user experience design including key stages of the design process, design ethics, and the methods and tools involved at each stage of design. Conduct formative research on clients, users, use contexts and tasks. Gain experience with user research methodologies and learn to create intermediate design tools such as personas. Develop and present a design proposal for a chosen project. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Declared in Information MS, Design + Innovation MS , or Capstone Certificate in User Experience DesignCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Apply fundamental concepts and practices of user experience design Audience: Graduate 2. Understand the ethics of design including practices of inclusive design and accessibility Audience: Graduate 3. Conduct formative research to inform design Audience: Graduate 4. Apply common user data collection methods Audience: Graduate 5. Analyze and visualize processes across time and interfaces Audience: Graduate 6. Create and apply common UX design tools such as personas, scenarios and user journey maps Audience: Graduate 7. Effectively convey the output of user research and initial design through oral and written communication. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/L I S 612 — USER EXPERIENCE DESIGN 2 3 credits. Advanced study of UX design. Introduces processes of ideation, key concepts of visual design, conceptual and interaction design, low and high-resolution prototyping of design techniques. Applications include drafting designs based on user models and initial testing of prototypes. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20611" title="COMP SCI/L I S 611" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 611');">COMP SCI/L I S 611</a> and Declared in Information MS, Design + Innovation MS, or Capstone Certificate in User Experience DesignCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Develop design ideas and communicate them through brainstorming, sketching, and modeling; Audience: Graduate 2. Create designs that follow principles of and best practices in visual and interaction design; Audience: Graduate 3. Prototype designs using rapid prototyping methods for communication and testing; Audience: Graduate 4. Understand human perceptual, cognitive, and motor processes involved in interaction; Audience: Graduate 5. Evaluate designs using expert- and empirical-evaluation methods; Audience: Graduate 6. Integrate design, prototyping, and evaluation methods and principles into a process toward addressing a design problem Audience: Graduate 7. Communicate their ideas to others, integrate feedback into their design work, and critique the work of others constructively. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/L I S 613 — USER EXPERIENCE DESIGN 3 3 credits. Conduct formal evaluations of the user experience (UX) or usability of a digital system. Gain familiarity with the evaluation and research process including key stages, tasks for each stage, common data collection and analysis methods, and common tools employed in the field. Gain experience with a variety of UX evaluation approaches. Collect pilot data and develop a proposal for further UX testing. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20612" title="COMP SCI/L I S 612" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 612');">COMP SCI/L I S 612</a> and Declared in Information MS, Design + Innovation MS, or Capstone Certificate in User Experience DesignCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Demonstrate understanding of a variety of UX testing approaches Audience: Graduate 2. Plan and implement all phases of testing for a digital system including planning, data collection, analysis and reporting Audience: Graduate 3. Demonstrate understanding of the relationship among research design, instruments, metrics, and data analysis Audience: Graduate 4. Implement major testing approaches such as task-based, information architecture and accessibility Audience: Graduate 5. Have knowledge of contemporary tools used for UX testing Audience: Graduate 6. Communicate evaluation findings effectively and use data to improve systems design Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/L I S 614 — USER EXPERIENCE DESIGN CAPSTONE 1 credit. Applies a design studio critique approach to produce a learning environment of collaborative and interdisciplinary peer critique and learning, in addition to provide expert feedback and suggestions. Present and defend the latest iteration of the user experience design project developed in earlier courses while learning about the professions associated with digital user experience design. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20613" title="COMP SCI/L I S 613" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 613');">COMP SCI/L I S 613</a> and declared in Design + Innovation MS, or the Capstone Certificate in User Experience DesignCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Summer 2024<div class="bubblehide">Learning Outcomes: 1. Knowledge of, and ability to apply, data collection and analysis methodologies for user experience research. Audience: Graduate 2. Knowledge of, and ability to apply, design principles and user behavior theories to digital environments. Audience: Graduate 3. Create, critique and revise design prototypes based on testing data and feedback Audience: Graduate 4. Effectively plan, manage and communicate a user experience design project. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 620 — COMPUTER SCIENCES CAPSTONE 3 credits. Build a meaningful product from start to finish with a local, regional, national and international corporate client that solves a real-world problem. In a collaborative space design, develop, test, debug, document, and deliver a software project for a corporate client, learning and using new technologies and agile software development techniques. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a>, senior standing, and declared in an undergraduate Computer Sciences majorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Evaluate and utilize agile techniques such as Design Thinking and Scrum. Audience: Undergraduate 2. Demonstrate effective teamwork skills. Audience: Undergraduate 3. Develop a software product for a corporate client from start to finish including phases for discovery, design, coding, testing, and documentation. Audience: Undergraduate 4. Execute presentation skills by holding weekly demos with corporate client. Audience: Undergraduate 5. Examine the types of roles typically present in an agile software development organization such as Product Manager, UX, Scrum Master, engineers, testers, DevOps, etc Audience: Undergraduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E 635 — TOOLS AND ENVIRONMENTS FOR OPTIMIZATION 3 credits. Formulation and modeling of applications from computer sciences, operations research, business, science and engineering involving optimization and equilibrium models. Survey and appropriate usage of software tools for solving such problems, including modeling language use, automatic differentiation, subroutine libraries and web-based optimization tools and environments. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: (<a href="/search/?P=MATH%20320" title="MATH 320" class="bubblelink code" onclick="return showCourse(this, 'MATH 320');">MATH 320</a>, <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">340</a>, <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">341</a>, or <a href="/search/?P=MATH%20375" title="MATH 375" class="bubblelink code" onclick="return showCourse(this, 'MATH 375');">375</a>) and (<a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, or <a href="/search/?P=L%20I%20S%20102" title="L I S/COMP SCI 102" class="bubblelink code" onclick="return showCourse(this, 'L I S 102');">L I S/COMP SCI 102</a> (COMP SCI 202 prior to Fall 2023)); grad/professional standing; declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Physical Sci. Counts toward the Natural Sci req Level - Intermediate L&S Credit - Counts as Liberal Arts and Science credit in L&S Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2015</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 638 — UNDERGRADUATE TOPICS IN COMPUTING 1-4 credits. Selected topics in computing. Each offering of the course will cover a topic selected by the instructor and may cover one or more topics from all of computer science. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>, <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">300</a>, 301, 302, <a href="/search/?P=COMP%20SCI%20310" title="COMP SCI 310" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 310');">310</a>, 367, placement into <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>, or <a href="/search/?P=L%20I%20S%20102" title="L I S/COMP SCI 102" class="bubblelink code" onclick="return showCourse(this, 'L I S 102');">L I S/COMP SCI 102</a> (COMP SCI 202 prior to Fall 2023), graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Breadth - Natural Science Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 639 — UNDERGRADUATE ELECTIVE TOPICS IN COMPUTING 3-4 credits. Selected topics in computing. Each offering of the course will cover a topic selected by the instructor. Offerings of this course will provide sufficient depth into their subject to count as electives to meet CS Major requirements. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: NoneCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 640 — INTRODUCTION TO COMPUTER NETWORKS 3 credits. Architecture of computer networks and network protocols, protocol layering, reliable transmission, congestion control, flow control, naming and addressing, unicast and multicast routing, network security, network performance widely used protocols such as Ethernet, wireless LANs, IP, TCP, and HTTP. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20537" title="COMP SCI 537" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 537');">COMP SCI 537</a> or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 642 — INTRODUCTION TO INFORMATION SECURITY 3 credits. Senior level undergraduate course covering various topics on information security. Covers a wide range of topics, such as cryptographic primitives, security protocols, system security, and emerging topics. Elementary knowledge of mathematical logic and discrete probability theory needed, such as <a href="/search/?P=MATH%20240" title="MATH/COMP SCI 240" class="bubblelink code" onclick="return showCourse(this, 'MATH 240');">MATH/COMP SCI 240</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20537" title="COMP SCI 537" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 537');">COMP SCI 537</a> or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for ProfessionalsCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 681 — SENIOR HONORS THESIS 3 credits. Individual study for seniors completing theses for honors in the Computer Sciences major as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors - Honors Only Courses (H)Repeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 682 — SENIOR HONORS THESIS 3 credits. Individual study for seniors completing theses for honors in the Computer Sciences major as arranged with a faculty member. Continuation of <a href="/search/?P=COMP%20SCI%20681" title="COMP SCI 681" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 681');">COMP SCI 681</a> <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&S Honors - Honors Only Courses (H)Repeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 691 — SENIOR THESIS 2-3 credits. Individual study for seniors completing theses as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 692 — SENIOR THESIS 2-3 credits. Individual study for seniors completing theses as arranged with a faculty member, continuation of <a href="/search/?P=COMP%20SCI%20691" title="COMP SCI 691" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 691');">COMP SCI 691</a> <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 698 — DIRECTED STUDY 1-6 credits. Directed study projects for juniors and seniors as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 699 — DIRECTED STUDY 1-6 credits. Directed study projects for juniors and seniors as arranged with a faculty member. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Level - Advanced L&S Credit - Counts as Liberal Arts and Science credit in L&SRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 701 — CONSTRUCTION OF COMPILERS 3 credits. Principles of the design and implementation of programming languages. Topics include: Principles of compilation, static program analysis, compilation methods to support profiling, and code-generation methods. Knowledge of programming languages and compiler design strongly encouraged, such as <a href="/search/?P=COMP%20SCI%20536" title="COMP SCI 536" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 536');">COMP SCI 536</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2020</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 702 — GRADUATE COOPERATIVE EDUCATION 1-2 credits. A full-time cooperative experience (10 or 5 week option) in computer science in a commercial, government, or nonprofit setting, usually in the form of an internship. The work will be monitored by a graduate advisor from the faculty of the Department of Computer Sciences. 10 week option for 2 credits, 5 week option for 1 credit. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, for 2 number of completionsLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Understand the nature and demands of a professional career in computer science. Audience: Graduate 2. Apply knowledge gained in computer science coursework in a professional setting. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 703 — PROGRAM VERIFICATION AND SYNTHESIS 3 credits. Formal methods for program verification. Model-checking techniques; linear temporal logic; computational tree logic; logic/automata connection; bisimulations; probabilistic model-checking. Special topics include: program synthesis, verification and synthesis of privacy properties. Knowledge of programming languages and compiler design strongly encouraged, such as <a href="/search/?P=COMP%20SCI%20536" title="COMP SCI 536" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 536');">COMP SCI 536</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023<div class="bubblehide">Learning Outcomes: 1. Logic: Syntax and formal semantics of temporal logics such as LTL and MSO and formalizing software safety properties. Translations from temporal logics to automata. Audience: Graduate 2. Model Checking: Understand algorithms for checking whether a program satisfies a temporal property in temporal logic and apply such algorithms to verification problems. Audience: Graduate 3. Synthesis: Understand and apply state-of-the-art program synthesis algorithms such as counterexample-guided inductive synthesis, syntax-guided synthesis, sketching, and MCMC sampling. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 704 — PRINCIPLES OF PROGRAMMING LANGUAGES 3 credits. Introduction to principles of advanced programming languages and programming-language theory. Topics include: lambda-calculus, functional languages, polymorphic functions, type inference, structural induction, lazy evaluation, operational semantics, denotational semantics, and axiomatic semantics. Students are strongly encouraged to have knowledge of programming languages, such as from <a href="/search/?P=COMP%20SCI%20536" title="COMP SCI 536" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 536');">COMP SCI 536</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 706 — ANALYSIS OF SOFTWARE ARTIFACTS 3 credits. Advanced course covering various analysis techniques used in software engineering. Covers techniques for analyzing various software artifacts. Some of the topics that will be covered are: model checking, testing, program analysis, requirements analysis, and safety analysis. Students are strongly encouraged to have knowledge of programming languages and compiler design, such as <a href="/search/?P=COMP%20SCI%20536" title="COMP SCI 536" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 536');">COMP SCI 536</a>, and a basic knowledge of mathematical logic. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2017</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 707 — MOBILE AND WIRELESS NETWORKING 3 credits. Design and implementation of protocols, systems, and applications for mobile and wireless networking, particularly at the media access control, network, transport, and application layers. Focus is on the unique problems and challenges presented by the properties of wireless transmission, various device constraints such as limited battery power, and node mobility. Knower of computer networking is strongly encouraged, such as from <a href="/search/?P=COMP%20SCI%20640" title="COMP SCI 640" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 640');">COMP SCI 640</a> or <a href="/search/?P=E%20C%20E%20537" title="E C E 537" class="bubblelink code" onclick="return showCourse(this, 'E C E 537');">E C E 537</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 710 — COMPUTATIONAL COMPLEXITY 3 credits. Study of the capabilities and limitations of efficient computation. Relationships between models representing capabilities such as parallelism, randomness, quantum effects, and non-uniformity; and models based on the notions of nondeterminism, alternation, and counting, which capture the complexity of important problems. Knowledge of the theory of computation is strongly encouraged, such as <a href="/search/?P=COMP%20SCI%20520" title="COMP SCI 520" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 520');">COMP SCI 520</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH 714 — METHODS OF COMPUTATIONAL MATHEMATICS I 3 credits. Development of finite difference methods for hyperbolic, parabolic and elliptic partial differential equations. Analysis of accuracy and stability of difference schemes. Direct and iterative methods for solving linear systems. Introduction to finite volume methods. Applications from science and engineering. Students are strongly encouraged to have programming skills (e.g. <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>) and some undergraduate numerical analysis (e.g. <a href="/search/?P=MATH%20514" title="MATH/COMP SCI 514" class="bubblelink code" onclick="return showCourse(this, 'MATH 514');">MATH/COMP SCI 514</a> or <a href="/search/?P=COMP%20SCI%20412" title="COMP SCI 412" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 412');">COMP SCI 412</a>), analysis and differential equations (e.g. <a href="/search/?P=MATH%20322" title="MATH 322" class="bubblelink code" onclick="return showCourse(this, 'MATH 322');">MATH 322</a> and <a href="/search/?P=MATH%20521" title="MATH 521" class="bubblelink code" onclick="return showCourse(this, 'MATH 521');">MATH 521</a>) and linear algebra (e.g. <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">MATH 341</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/MATH 715 — METHODS OF COMPUTATIONAL MATHEMATICS II 3 credits. Introduction to spectral methods (Fourier, Chebyshev, Fast Fourier Transform), finite element methods (Galerkin methods, energy estimates and error analysis), and mesh-free methods (Monte-Carlo, smoothed-particle hydrodynamics) for solving partial differential equations. Applications from science and engineering. Applications from science and engineering. Students are strongly encouraged to have programming skills (e.g. <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a>), undergraduate numerical analysis (e.g. <a href="/search/?P=MATH%20514" title="MATH/COMP SCI 514" class="bubblelink code" onclick="return showCourse(this, 'MATH 514');">MATH/COMP SCI 514</a> or <a href="/search/?P=COMP%20SCI%20412" title="COMP SCI 412" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 412');">COMP SCI 412</a>), analysis (<a href="/search/?P=MATH%20322" title="MATH 322" class="bubblelink code" onclick="return showCourse(this, 'MATH 322');">MATH 322</a> and <a href="/search/?P=MATH%20521" title="MATH 521" class="bubblelink code" onclick="return showCourse(this, 'MATH 521');">521</a>) and linear algebra (e.g. <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">MATH 341</a> or equiv.) <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) ProgramCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E 719 — STOCHASTIC PROGRAMMING 3 credits. Stochastic programming is concerned with decision making in the presence of uncertainty, where the eventual outcome depends on a future random event. Topics include modeling uncertainty in optimization problems, risk measures, stochastic programming algorithms, approximation and sampling methods, and applications. Students are strongly encouraged to have knowledge of linear programming (e.g., <a href="/search/?P=MATH%20525" title="MATH/COMP SCI/I SY E/STAT 525" class="bubblelink code" onclick="return showCourse(this, 'MATH 525');">MATH/COMP SCI/I SY E/STAT 525</a>) and probability and statistics (e.g., <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">MATH/STAT 431</a>). Knowledge of integer optimization (<a href="/search/?P=MATH%20728" title="MATH/COMP SCI/I SY E 728" class="bubblelink code" onclick="return showCourse(this, 'MATH 728');">MATH/COMP SCI/I SY E 728</a>) is helpful, but not required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023<div class="bubblehide">Learning Outcomes: 1. Learn the terms, basic capabilities, and limitations of stochastic programming models Audience: Graduate 2. Formulate stochastic programming models Audience: Graduate 3. Implement the algorithms used to solve stochastic programming problems Audience: Graduate 4. Learn principles of decomposition algorithms for solving large-scale optimization problems Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I/E C E/MED PHYS 722 — COMPUTATIONAL OPTICS AND IMAGING 3 credits. Computational imaging includes all imaging methods that produce images as a result of computation on collected signals. Learn the tools to design new computational imaging methods to solve specific imaging problems. Provides an understanding of the physics of light propagation and measurement, and the computational tools to model it, including wave propagation, ray tracing, the radon transform, and linear algebra using matrix and integral operators and the computational tools to reconstruct an image, including linear inverse problems, neural networks, convex optimization, and filtered back-projection. Covers a variety of example computational imaging techniques and their applications including coded apertures, structured illumination, digital holography, computed tomography, imaging through scattering media, compressed sensing, and non-line-of-sight imaging. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Apply ray and wave based light propagation models Audience: Graduate 2. Explain the process of image formation in conventional imaging systems using theory and computational models Audience: Graduate 3. Select and combine the different components required in an imaging system to perform light manipulation, collection, and image reconstruction Audience: Graduate 4. Apply the linear matrix and integral operators that model light propagation Audience: Graduate 5. Apply the linear inverse algorithms that allow an imaging system to reconstruct properties of the scene from collected data Audience: Graduate 6. Simulate different computational imaging systems and perform computation on simulated datasets Audience: Graduate 7. Understand the most common computational imaging techniques and be able to use and adapt them for their own applications Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E 723 — DYNAMIC PROGRAMMING AND ASSOCIATED TOPICS 3 credits. General and special techniques of dynamic programming developed by means of examples. Shortest-path algorithms. Deterministic equipment replacement models. Resource allocation problem. Traveling-salesman problem. Knapsack problem. Analysis of inventory systems. General stochastic formulations. Markovian decision processes. Students are strongly encouraged to have knowledge of mathematical optimization (e.g., <a href="/search/?P=COMP%20SCI%20525" title="COMP SCI/I SY E/MATH/STAT 525" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 525');">COMP SCI/I SY E/MATH/STAT 525</a>, I SY E 623, <a href="/search/?P=COMP%20SCI%20726" title="COMP SCI/I SY E/MATH/STAT 726" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 726');">COMP SCI/I SY E/MATH/STAT 726</a>), knowledge of analysis (e.g., <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">MATH/STAT 431</a> or <a href="/search/?P=MATH%20521" title="MATH 521" class="bubblelink code" onclick="return showCourse(this, 'MATH 521');">521</a>) and programming ability (e.g., <a href="/search/?P=COMP%20SCI%20200" title="COMP SCI 200" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 200');">COMP SCI 200</a> or 301) <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify basic components, such as the state space, of a dynamic program Audience: Graduate 2. Formulate and solve dynamic programs under different performance criteria such as finite horizon discounted reward/cost criteria Audience: Graduate 3. Apply dynamic programming tools and concepts in `traditional' industrial engineering applications such as supply chain, manufacturing, and healthcare Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/MATH/STAT 726 — NONLINEAR OPTIMIZATION I 3 credits. Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Line-search and trust-region methods; quasi-Newton methods; conjugate-gradient and limited-memory methods for large-scale problems; derivative-free optimization; algorithms for least-squares problems and nonlinear equations; gradient projection algorithms for bound-constrained problems; and simple penalty methods for nonlinearly constrained optimization. Students are strongly encouraged to have knowledge of linear algebra and familiarity with basic mathematical analysis. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E 727 — CONVEX ANALYSIS 3 credits. Convex sets in finite-dimensional spaces: relative interiors, separation, set operations. Convex functions: conjugacy, subdifferentials and directional derivations, functional operations, Fenchel-Rockafellar duality. Applications to operations research and related areas. Students taking this course are strongly encouraged to have had a course in basic analysis (e.g. <a href="/search/?P=MATH%20521" title="MATH 521" class="bubblelink code" onclick="return showCourse(this, 'MATH 521');">MATH 521</a>) and a course in linear algebra (e.g., <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">MATH 340</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2020</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/MATH 728 — INTEGER OPTIMIZATION 3 credits. Introduces optimization problems over integers, and surveys the theory behind the algorithms used in state-of-the-art methods for solving such problems. Special attention is given to the polyhedral formulations of these problems, and to their algebraic and geometric properties. Applicability of Integer Optimization is highlighted with applications in combinatorial optimization. Key topics include: formulations, relaxations, polyhedral theory, cutting planes, decomposition, enumeration. Students are strongly encouraged to have knowledge of Linear Programming (e.g., <a href="/search/?P=MATH%20525" title="MATH/COMP SCI/I SY E/STAT 525" class="bubblelink code" onclick="return showCourse(this, 'MATH 525');">MATH/COMP SCI/I SY E/STAT 525</a>), including algorithms, duality and polyhedral theory. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Describe and explain the basics of polyhedral theory, which consists in the study of systems of linear inequalities both from an algebraic and a geometric point of view Audience: Graduate 2. Define perfect formulations and identify what properties are desirable in an integer programming formulation of a problem Audience: Graduate 3. Explain how valid inequalities can be used as cutting planes to strengthen integer programming formulations Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/I SY E/MATH 730 — NONLINEAR OPTIMIZATION II 3 credits. Theory and algorithms for nonlinearly constrained optimization. Relevant geometric concepts, including tangent and normal cones, theorems of the alternative, and separation results. Constraint qualifications. Geometric and algebraic expression of first-order optimality conditions. Second-order optimality conditions. Duality. Nonlinear programming algorithms: merit functions and filters; interior-point, augmented Lagrangian, and sequential quadratic programming algorithms. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=STAT%20726" title="STAT/COMP SCI/I SY E/MATH 726" class="bubblelink code" onclick="return showCourse(this, 'STAT 726');">STAT/COMP SCI/I SY E/MATH 726</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 736 — ADVANCED OPERATING SYSTEMS 3 credits. Advanced topics in operating systems, including process communication, resource allocation, multiprocess and network operating systems, kernel philosophies, fault-tolerant systems, virtual machines, high-level language systems, verifiability and proof techniques. Comp Sci 537 or cons inst <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 739 — DISTRIBUTED SYSTEMS 3 credits. Basic concepts; distributed programming; distributed file systems; atomic actions; fault tolerance, transactions, program and data replication, recovery; distributed machine architectures; security and authentication; load balancing and process migration; distributed debugging; distributed performance measurement; distributed simulation techniques; distributed applications; correctness considerations and proof systems. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=COMP%20SCI%20736" title="COMP SCI 736" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 736');">COMP SCI 736</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 740 — ADVANCED COMPUTER NETWORKS 3 credits. Advanced topics in computer communications networks: congestion and flow control; routing; rate-based protocols; high speed interfaces and technologies: metropolitan area networks; fast packet switching technologies; advanced applications; network services: name service, authentication, resource location. Students are strongly encouraged to have knowledge of computer network design and protocols (e.g., <a href="/search/?P=COMP%20SCI%20640" title="COMP SCI 640" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 640');">COMP SCI 640</a>) <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 744 — BIG DATA SYSTEMS 3 credits. Issues in the design and implementation of big data processing systems, including: an overview of cluster architecture, key design goals (flexibility, performance and fault tolerance), popular execution frameworks, basic abstractions, and applications (e.g., batch analytics, stream processing, graph processing, and machine learning). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 750 — REAL-TIME COMPUTING SYSTEMS 3 credits. Introduction to the unique issues in the design and analysis of computer systems for real-time applications. Hardware and software support for guaranteeing timeliness with and without failures. Resource management, time-constrained communication, scheduling and imprecise computations, real-time kernels and case studies. Students are strongly encouraged to have knowledge of computer architecture (e.g., <a href="/search/?P=E%20C%20E%20552" title="E C E/COMP SCI 552" class="bubblelink code" onclick="return showCourse(this, 'E C E 552');">E C E/COMP SCI 552</a>) and operating system functions (e.g., <a href="/search/?P=COMP%20SCI%20537" title="COMP SCI 537" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 537');">COMP SCI 537</a>) <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 752 — ADVANCED COMPUTER ARCHITECTURE I 3 credits. Processor design, computer arithmetic, pipelining, multi-operation processors, vector processors, control units, precise interrupts, main memory, cache memories, instruction set design, stack machines, busses and I/O, protection and security. Students are strongly encouraged to have knowledge of computer architecture (e.g., <a href="/search/?P=E%20C%20E%20552" title="E C E/COMP SCI 552" class="bubblelink code" onclick="return showCourse(this, 'E C E 552');">E C E/COMP SCI 552</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 755 — VLSI SYSTEMS DESIGN 3 credits. Overview of MOS devices and circuits; introduction to integrated circuit fabrication; topological design of data flow and control; interactive graphics layout; circuit simulation; system timing; organizational and architectural considerations; alternative implementation approaches; design project. <a href="/search/?P=E%20C%20E%20555" title="E C E 555" class="bubblelink code" onclick="return showCourse(this, 'E C E 555');">E C E 555</a> or equivalent experience is strongly recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 756 — COMPUTER-AIDED DESIGN FOR VLSI 3 credits. Broad introduction to computer-aided design tools for VLSI, emphasizing implementation algorithms and data structures. Topics covered: design styles, layout editors, symbolic compaction, module generators, placement and routing, automatic synthesis, design-rule checking, circuit extraction, simulation and verification. Students are strongly encourage to have programming skills and to have taken a course in Digital System Fundamentals such as <a href="/search/?P=E%20C%20E%20352" title="E C E/COMP SCI 352" class="bubblelink code" onclick="return showCourse(this, 'E C E 352');">E C E/COMP SCI 352</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 757 — ADVANCED COMPUTER ARCHITECTURE II 3 credits. Parallel algorithms, principles of parallelism detection and vectorizing compilers, interconnection networks, MIMD machines, processor synchronization, data coherence, multis, dataflow machines, special purpose processors. Students are strongly encouraged to have knowledge of computer architecture (e.g., <a href="/search/?P=E%20C%20E%20552" title="E C E/COMP SCI 552" class="bubblelink code" onclick="return showCourse(this, 'E C E 552');">E C E/COMP SCI 552</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 758 — ADVANCED TOPICS IN COMPUTER ARCHITECTURE 3 credits. Advanced topics in computer architecture that explore the implications to architecture of forthcoming evolutionary and revolutionary changes in application demands, software paradigms, and hardware implementation technologies. Students are strongly encouraged to have knowledge of computer architecture (e.g., <a href="/search/?P=E%20C%20E%20552" title="E C E/COMP SCI 552" class="bubblelink code" onclick="return showCourse(this, 'E C E 552');">E C E/COMP SCI 552</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/E M A/E P/M E 759 — HIGH PERFORMANCE COMPUTING FOR APPLICATIONS IN ENGINEERING 3 credits. An overview of hardware and software solutions that enable the use of advanced computing in tackling computationally intensive Engineering problems. Hands-on learning promoted through programming assignments that leverage emerging hardware architectures and use parallel computing programming languages. Students are strongly encourage to have completed COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20400" title="COMP SCI 400" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 400');">COMP SCI 400</a> or to have equivalent experience. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 760 — MACHINE LEARNING 3 credits. Computational approaches to learning: including inductive inference, explanation-based learning, analogical learning, connectionism, and formal models. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Cognitive modeling and relevant psychological results. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify different aspects of machine learning, including supervised learning, unsupervised learning, and reinforcement learning Audience: Graduate 2. Implement and analyze a variety of supervised models for classification and regression, including decision trees, instance-based models, naive Bayes, support vector machines, a variety of neural networks, linear and logistic regression, and others Audience: Graduate 3. Implement and analyze neural network models, starting with the perceptron, and continuing to multilayer perceptrons, convolutional neural networks, recurrent neural networks, along with deep generative models Audience: Graduate 4. Identify various types of regularization techniques and their properties Audience: Graduate 5. Implement optimization techniques used in modern machine learning, including gradient descent and stochastic gradient descent Audience: Graduate 6. Apply various concepts and metrics involved in evaluating models: accuracy, F measures, ROC, and precision/recall curves, and implement cross-validation Audience: Graduate 7. Analyze unsupervised learning techniques for clustering, dimensionality reduction, and latent models Audience: Graduate 8. Identify classical and modern techniques to improve models or deal with dearth of data: ensemble methods, semi-supervised learning, weak supervision Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 761 — MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING 3 credits. Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of state-of-the-art learning algorithms and optimization methods, and applications of machine learning. Knowledge of probability [such as <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">MATH/STAT 431</a> or <a href="/search/?P=COMP%20SCI%20561" title="COMP SCI/E C E 561" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 561');">COMP SCI/E C E 561</a>] and linear algebra [such as <a href="/search/?P=MATH%20341" title="MATH 341" class="bubblelink code" onclick="return showCourse(this, 'MATH 341');">MATH 341</a> or <a href="/search/?P=M%20E%20532" title="M E/COMP SCI/E C E 532" class="bubblelink code" onclick="return showCourse(this, 'M E 532');">M E/COMP SCI/E C E 532</a>] is required. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Derive and apply mathematical tools for machine learning from probability, statistics, linear algebra, and optimization Audience: Graduate 2. Perform mathematical analysis and characterization of generative and discriminative models Audience: Graduate 3. Perform mathematical analysis of machine learning algorithms Audience: Graduate 4. Perform derivation of basic machine learning error bounds and related performance analysis Audience: Graduate 5. Read and understand theoretical papers from machine learning conferences Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 762 — ADVANCED DEEP LEARNING 3 credits. Explore methods and applications of deep learning. Covers cutting-edge topics, including neural architecture design, robustness and reliability of deep learning, learning with less supervision, lifelong machine learning, deep generative modeling, theoretical understanding of deep learning, and interpretable deep learning. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20760" title="E C E/COMP SCI 760" class="bubblelink code" onclick="return showCourse(this, 'E C E 760');">E C E/COMP SCI 760</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2023<div class="bubblehide">Learning Outcomes: 1. Understand the key elements and methods in the design and use of deep neural networks; Audience: Graduate 2. Advance knowledge and get exposure to cutting-edge topics in AI and deep learning Audience: Graduate 3. Explore new research directions and applications of deep learning Audience: Graduate 4. Identify and participate in original research in a collaborative team Audience: Graduate 5. Search for sources of information and evaluation methods and tools relevant to the project Audience: Graduate 6. Get hands-on experience in writing software and programs using popular deep learning libraries and frameworks Audience: Graduate 7. Develop analytic and problem-solving skills using computational approaches Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 763 — SECURITY AND PRIVACY FOR DATA SCIENCE 3 credits. Security and privacy concerns in data science. Three core subjects will be considered: Differential privacy and algorithmic fairness; Adversarial machine learning; and Applied cryptography, especially with applications to machine learning. In addition, a selection of more advanced topics will be covered. Possible examples include additional notions of privacy, language-based security, robust optimization. A firm grasp of probability/statistics (<a href="/search/?P=STAT%20431" title="STAT/MATH 431" class="bubblelink code" onclick="return showCourse(this, 'STAT 431');">STAT/MATH 431</a>) is recommended. Previous exposure to at least one of cryptography (<a href="/search/?P=COMP%20SCI%20435" title="COMP SCI/E C E/MATH 435" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 435');">COMP SCI/E C E/MATH 435</a>), security (<a href="/search/?P=COMP%20SCI%20642" title="COMP SCI 642" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 642');">COMP SCI 642</a>), and modern machine learning (<a href="/search/?P=COMP%20SCI%20539" title="COMP SCI/E C E/M E 539" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 539');">COMP SCI/E C E/M E 539</a> or <a href="/search/?P=COMP%20SCI%20540" title="COMP SCI 540" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 540');">540</a>) is also recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Evaluate machine learning systems from an adversarial, security and privacy mindset. Audience: Graduate 2. Identify common pitfalls and problems in ensuring security and privacy. Audience: Graduate 3. Summarize the commonalities and differences between notions of security and privacy (e.g., the difference between privacy and cryptographic security). Audience: Graduate 4. Explain the strengths and limitations of candidate definitions of security, privacy, and fairness properties in data science. Audience: Graduate 5. Apply useful primitives from applied cryptography to machine learning systems. Audience: Graduate 6. Use modern tools to design attacks and implement defensive measures. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 764 — TOPICS IN DATABASE MANAGEMENT SYSTEMS 3 credits. Implementation of database management systems, the impact of new technology on database management systems, back-end database computers, distributed database management systems, concurrency control, and query execution in both distributed and centralized systems, implementation of multiple user views, roll-back and recovery mechanisms, database translation. Students are strongly encouraged to have knowledge of database design (e.g., <a href="/search/?P=COMP%20SCI%20564" title="COMP SCI 564" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 564');">COMP SCI 564</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 765 — DATA VISUALIZATION 3 credits. Principles of the visual presentation of data. Survey of Information Visualization, Scientific Visualization, and Visual Analytics. Design and evaluation of visualizations and interactive exploration tools. Introduction to relevant foundations in visual design, human perception, and data analysis. Encodings, layout and interaction. Approaches to large data sets. Visualization of complex data types such as scalar fields, graphs, sets, texts, and multi-variate data. Use of 2D, 3D and motion in data presentations. Implementation issues. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 766 — COMPUTER VISION 3 credits. Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures. Students are strongly encouraged to have basic proficiency in calculus and linear algebra, such as <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">MATH 340</a>, and basic programming such as <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Develop basic computer vision applications using a programming environment Audience: Graduate 2. Formulate computer vision research problems motivated from real-world applications Audience: Graduate 3. Evaluate and compare existing solutions to a computer vision problem Audience: Graduate 4. Design approaches for solving computer vision problems based on a broad range of fundamental concepts in 2D and 3D computer vision, sensing and recognition Audience: Graduate 5. Communicate solutions verbally and in writing to justify choices while designing solutions Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 767 — COMPUTATIONAL METHODS FOR MEDICAL IMAGE ANALYSIS 3 credits. Study of computational techniques that facilitate automated analysis, manipulation, denoising, and improvement of large-scale and high resolution medical images. Design and implementation of methods from computer Vision and Machine Learning to efficiently process such image data to answer biologically and clinically meaningful scientific questions. Students are strongly encouraged to have programming skills and basic proficiency in calculus and linear algebra, such as <a href="/search/?P=MATH%20340" title="MATH 340" class="bubblelink code" onclick="return showCourse(this, 'MATH 340');">MATH 340</a>. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2016<div class="bubblehide">Learning Outcomes: 1. Recognize mathematical and computational aspects of modern image processing algorithms for image analysis and medical image processing Audience: Graduate 2. Interpret papers in the mainstream medical image analysis venues Audience: Graduate 3. Develop expertise in implementing algorithms described in research papers (or appropriate modifications) in a high level programming language on practical image processing tasks Audience: Graduate 4. Discuss algorithmic developments and experimental findings in the form of a research report for a technical audience Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 769 — ADVANCED NATURAL LANGUAGE PROCESSING 3 credits. Develop algorithms and mathematical models for natural language processing tasks, including text categorization, information retrieval, speech recognition, machine translation, and information extraction. Focus is on the state-of-the-art computational techniques as they are applied to natural language tasks. Students are strongly encouraged to have knowledge of introductory artificial intelligence (e.g., <a href="/search/?P=COMP%20SCI%20540" title="COMP SCI 540" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 540');">COMP SCI 540</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/ED PSYCH/PSYCH 770 — HUMAN-COMPUTER INTERACTION 3 credits. Principles of human-computer interaction (HCI); human subjects research methods and procedures, qualitative and quantitative data analysis; and semester-long research project situated in critical domains of HCI, including applications in ubiquitous, affective, assistive, social, and embodied computing. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 771 — LEARNING BASED METHODS FOR COMPUTER VISION 3 credits. Addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. Introduces deep learning in the context of computer vision. Covers topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. Emphasizes the design of vision and learning algorithms and models, as well as their practical implementations. Strongly recommended to have knowledge in computer vision or machine learning [such as <a href="/search/?P=COMP%20SCI%20540" title="COMP SCI 540" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 540');">COMP SCI 540</a>] or medical image analysis [such as B M I / <a href="/search/?P=COMP%20SCI%20567" title="COMP SCI/B M I 567" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 567');">COMP SCI/B M I 567</a>]. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Demonstrate their understanding of basic theories, current approaches, key concepts, and common practices in the area of deep learning for computer vision. Audience: Graduate 2. Recognize and distinguish among a variety of visual recognition problems in computer vision, including their problem formulations and evaluation metrics. Audience: Graduate 3. Utilize and implement deep learning models to solve visual recognition problems. Audience: Graduate 4. Design deep learning models for visual recognition problems, conduct experiments to evaluate the proposed model, and analyze and interpret the results. Audience: Graduate 5. Communicate effectively through written reports, oral presentations, and discussions. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 774 — DATA EXPLORATION, CLEANING, AND INTEGRATION FOR DATA SCIENCE 3 credits. Big Data is often said to deal with four Vs: volume, velocity, variety, and veracity. The focus is on variety and veracity challenges, which often arise in data science projects. In many such projects, data is often incorrect, hard to understand, and come from a variety of sources. Data scientists often spend 80% of their effort to explore, clean, and integrate this data, before analysis can be carried out to extract insights. As a result, managing variety and veracity has received significant attention. Study these topics, understand their challenges, and discuss solutions. These solutions often require data management, machine learning, big data scaling, cloud, crowdsourcing, and user interaction techniques. Knowledge of machine learning/AI [<a href="/search/?P=COMP%20SCI%20540" title="COMP SCI 540" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 540');">COMP SCI 540</a>], databases [<a href="/search/?P=COMP%20SCI%20564" title="COMP SCI 564" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 564');">COMP SCI 564</a>] and Python [<a href="/search/?P=COMP%20SCI%20320" title="COMP SCI 320" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 320');">COMP SCI 320</a>] recommended. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: No<div class="bubblehide">Learning Outcomes: 1. Identify and examine the key challenges of managing variety and veracity with large data sets. These include data acquisition, data extraction, data exploration, cleaning, matching, and merging data. Audience: Graduate 2. Summarize the variety and veracity solution approaches in academic and industry. Audience: Graduate 3. Design and apply course concepts to experiential learning through a research project. Audience: Graduate 4. Effectively communicate through written reports, oral presentations, and discussions. Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 775 — COMPUTATIONAL NETWORK BIOLOGY 3 credits. Introduces networks as a powerful representation in many real-world domains including biology and biomedicine. Encompasses theory and applications of networks, also referred to as graphs, to study complex systems such as living organisms. Surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery. Enables hands-on experience in network biology by implementing computational projects. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Recognize problems in molecular biology that are appropriate for network modeling Audience: Graduate 2. Identify appropriate network algorithms for different tasks Audience: Graduate 3. Evaluate the strengths and weaknesses of different network algorithms designed for a specific biological problem Audience: Graduate 4. Gain practical experience in applying a select set of network algorithms on real data and evaluate its outputs Audience: Graduate 5. Understand the algorithmic and statistical concepts of different network-based approaches Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I 776 — ADVANCED BIOINFORMATICS 3 credits. Advanced course covering computational problems in molecular biology. The course will study algorithms for problems such as: modeling sequence classes and features, phylogenetic tree construction, gene-expression data analysis, protein and RNA structure prediction, and whole-genome analysis and comparisons. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024<div class="bubblehide">Learning Outcomes: 1. Select and implement appropriate algorithms and probabilistic models for analyzing genomes, RNA, proteins, and biological networks Audience: Graduate 2. Write a manuscript describing a bioinformatics research study, including the motivation for the research, the scientific outcomes, and the detailed methods required to reproduce the study Audience: Graduate 3. Read a bioinformatics research paper to identify the key computational techniques and assess the evidence supporting the paper's claims Audience: Graduate 4. Identify emerging biological data types and data processing (e.g., single cell biology) and how the data can contribute to their research Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E 782 — ADVANCED COMPUTER SECURITY AND PRIVACY 3 credits. Security and privacy issues in software, networks, and hardware systems. Security vulnerabilities, privacy threats, threats modeling, and mitigation strategies. Privacy issues related to user interaction with devices, online systems, and networks. In addition, a selection of more advanced topics will be covered. Possible examples include applied cryptography in the context of systems, security and privacy policies, user authentication, and cyber-physical systems. Builds on prior experiences with one or more of the following: networking, security, modern machine learning, embedded systems, and mobile computing. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Identify contemporary research problems related to the security and privacy of modern computer systems Audience: Graduate 2. Implement known security attacks to identify weaknesses that led to those attacks and evaluate defense strategies Audience: Graduate 3. Differentiate among the different dimensions involved in protecting users’ security and privacy as they relate to effectiveness, practicality, and usability Audience: Graduate 4. Analyze, interpret, and critique research papers from top-tier security conferences by identifying their strengths and weaknesses Audience: Graduate 5. Propose original research by defining a problem, outlining a plan, performing the original research, and designing, implementing, and evaluating the proposed solution Audience: Graduate 6. Work effectively in teams to complete a research project Audience: Graduate 7. Communicate effectively through written reports, oral presentations, and discussion Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 784 — FOUNDATIONS OF DATA MANAGEMENT 3 credits. Foundational concepts in databases and data management. The first part of the course discusses topics on query languages (conjunctive queries, Datalog), their expressivity and complexity of evaluation. The second part studies advanced topics in modern data management, including data streams, massive parallelism, provenance, uncertain data management and privacy. There are no specific course prerequisites. It is strongly encouraged that the students are familiar with databases and relational algebra (<a href="/search/?P=COMP%20SCI%20564" title="COMP SCI 564" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 564');">COMP SCI 564</a> or equivalent). Knowledge of algorithms, complexity theory and probability will also be helpful. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 787 — ADVANCED ALGORITHMS 3 credits. Advanced paradigms for the design and analysis of efficient algorithms, including the use of randomness, linear programming, and semi-definite programming. Applications to data structures, approximating NP-hard optimization problems, learning, on-line and distributed problems. Students are strongly encouraged to have introductory knowledge of algorithms (e.g., <a href="/search/?P=COMP%20SCI%20577" title="COMP SCI 577" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 577');">COMP SCI 577</a>) <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 790 — MASTER'S THESIS 1-9 credits. Grad st; Master's candidates only <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 799 — MASTER'S RESEARCH 1-9 credits. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 809 — MATHEMATICAL TECHNIQUES IN THE ANALYSIS OF ALGORITHMS 3 credits. Techniques for quantitative analysis of algorithms. Charging arguments, amortization, probabilistic methods. Adversary and information lower bounds. Use of methods from combinatorics, complex analysis, and asymptotics in obtaining precise analyses of quicksort, chained hashing, and other algorithms. Students are strongly encouraged to have knowledge of algorithms (e.g., <a href="/search/?P=COMP%20SCI%20577" title="COMP SCI 577" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 577');">COMP SCI 577</a>) or applied math analysis (e.g., <a href="/search/?P=MATH%20321" title="MATH 321" class="bubblelink code" onclick="return showCourse(this, 'MATH 321');">MATH 321</a>) and theory of probability (e.g., <a href="/search/?P=MATH%20431" title="MATH/STAT 431" class="bubblelink code" onclick="return showCourse(this, 'MATH 431');">MATH/STAT 431</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2018</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 812 — ARITHMETIC ALGORITHMS 3 credits. Survey of algorithms and design paradigms for exact arithmetic, as used in public-key cryptography, computer algebra, and pseudo-random number generation. Topics include primality testing, factorization of integers and polynomials, discrete logarithms, and (optionally) elliptic curves and integer lattices. Students are strongly encourage to have knowledge of basic abstract algebra (e.g., <a href="/search/?P=MATH%20541" title="MATH 541" class="bubblelink code" onclick="return showCourse(this, 'MATH 541');">MATH 541</a>), and intermediate programming ability (e.g., COMP SCI 367 or <a href="/search/?P=COMP%20SCI%20300" title="COMP SCI 300" class="bubblelink code" onclick="return showCourse(this, 'COMP SCI 300');">COMP SCI 300</a>). <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 838 — TOPICS IN COMPUTING 1-3 credits. Advanced topics of special interest to students in various areas of Computer Science. Each offering of the course will cover a topic selected by the instructor. Credit varies by offering - check with the department to determine how an offering counts toward degree requirements. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Spring 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 839 — CORE TOPICS IN COMPUTING 3 credits. Topics selected from advanced areas. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M I/PSYCH 841 — COMPUTATIONAL COGNITIVE SCIENCE 3 credits. Studies the biological and computational basis of intelligence, by combining methods from cognitive science, artificial intelligence, machine learning, computational biology, and cognitive neuroscience. Requires ability to program. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Spring 2022</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/E C E/STAT 861 — THEORETICAL FOUNDATIONS OF MACHINE LEARNING 3 credits. Advanced mathematical theory and methods of machine learning. Statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, optimization, learning paradigms. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: <a href="/search/?P=E%20C%20E%20761" title="E C E/COMP SCI 761" class="bubblelink code" onclick="return showCourse(this, 'E C E 761');">E C E/COMP SCI 761</a> or <a href="/search/?P=E%20C%20E%20830" title="E C E 830" class="bubblelink code" onclick="return showCourse(this, 'E C E 830');">E C E 830</a>Course Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: NoLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 880 — TOPICS IN THEORETICAL COMPUTER SCIENCE 3 credits. Advanced topics in algorithms, complexity, and cryptography. The exact topic varies. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Graduate/professional standingCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 899 — PRE-DISSERTATOR RESEARCH 1-9 credits. Independent research supervised by a faculty member for students who have completed a master's degree but have not reached dissertator status. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 900 — ADVANCED SEMINAR IN COMPUTER SCIENCE 0-1 credits. Seminar on recent research on various aspects of computer science. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI/B M E/B M I/BIOCHEM/CBE/GENETICS 915 — COMPUTATION AND INFORMATICS IN BIOLOGY AND MEDICINE 1 credit. Participants and outside speakers will discuss current research in computation and informatics in biology and medicine. This seminar is required of all CIBM program trainees. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024<div class="bubblehide">Learning Outcomes: 1. Discuss how methods from computer science, statistics, information science and engineering are applied to problems in biology, medicine and population health Audience: Graduate 2. Recognize and be able to define applications in translational bioinformatics, clinical informatics and public health informatics Audience: Graduate</div></div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 990 — DISSERTATION 1-6 credits. Advanced level mentored reading and research for students with dissertator status. <button class="notinpdf cb-extras-toggle" aria-expanded="false" onclick="toggleCourseBlockInfo(this); return false;"><svg aria-hidden="true"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#uw-symbol-caret-down"></use></svg>View details</button><div class="cb-extras" aria-hidden="true">Requisites: Consent of instructorCourse Designation: Grad 50% - Counts toward 50% graduate coursework requirementRepeatable for Credit: Yes, unlimited number of completionsLast Taught: Fall 2024</div></div><style>.lfjsbubblemain .bubblehide {display: none !important;}.searchresult .bubblehide {display: none !important;}</style><div class="courseblock "> COMP SCI 999 — DISSERTATOR RESEARCH 1-6 credits. 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