Computer Science

CS 1C. Introduction to Computing at Stanford. 1 Unit.

For those who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by Student Technology. Final project. Not a programming course.
Same as: VPTL 1

CS 1U. Practical Unix. 1 Unit.

A practical introduction to using the Unix operating system with a focus on Linux command line skills. Class will consist of video tutorials and weekly hands-on lab sections. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Topics may be added, given sufficient interest. Course website: http://cs1u.stanford.edu.

CS 7. Personal Finance for Engineers. 1 Unit.

Introduction to the fundamentals and analysis specifically needed by engineers to make informed and intelligent financial decisions. Course will focus on actual industry-based financial information from technology companies and realistic financial issues. Topics include: behavioral finance, budgeting, debt, compensation, stock options, investing and real estate. No prior finance or economics experience required.

CS 9. Problem-Solving for the CS Technical Interview. 1 Unit.

This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Drawing on multiple sources of actual interview questions, students will learn key problem-solving strategies specific to the technical/coding interview. Students will be encouraged to synthesize information they have learned across different courses in the major. Emphasis will be on the oral and combination written-oral modes of communication common in coding interviews, but which are unfamiliar settings for problem solving for many students. Prerequisites: CS 106B or X.

CS 11SI. How to Make VR: Introduction to Virtual Reality Design and Development. 2 Units.

In this hands-on, experiential course, students will design and develop virtual reality applications. You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. The class will teach the design best-practices and the creation pipeline for VR applications. Students will work in groups to present a final project in building an application for the Oculus Go headset. Enrollment is limited and by application only. See https://cs11si.stanford.edu for more information. Prerequisite: CS 106A or equivalent.

CS 21SI. AI for Social Good. 2 Units.

Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. The class will focus on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of CS107, mathematical fluency at the level of CS103, comfort with probability at the level of CS109 (or equivalent). Application required for enrollment.

CS 22A. The Social & Economic Impact of Artificial Intelligence. 1 Unit.

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems are free of algorithmic bias and respect human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? The goal of CS22a is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Same as: INTLPOL 200

CS 24. Minds and Machines. 4 Units.

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Same as: LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200

CS 28. Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond. 2 Units.

Technical developments in artificial intelligence (AI) have opened up new opportunities for entrepreneurship, as well as raised profound longer term questions about how human societal and economic systems may be re­organized to accommodate the rise of intelligent machines. In this course, closely co­taught by a Stanford professor and a leading Silicon Valley venture capitalist, we will examine the current state of the art capabilities of existing artificial intelligence systems, as well as economic challenges and opportunities in early stage startups and large companies that could leverage AI. We will focus on gaps between business needs and current technical capabilities to identify high impact directions for the development of future AI technology. Simultaneously, we will explore the longer term societal impact of AI driven by inexorable trends in technology and entrepreneurship. The course includes guest lectures from leading technologists and entrepreneurs who employ AI in a variety of fields, including healthcare, education, self­driving cars, computer security, natural language interfaces, computer vision systems, and hardware acceleration.

CS 31N. Counterfactuals: The Science of What Ifs?. 3 Units.

How might the past have changed if different decisions were made? This question has captured the fascination of people for hundreds of years. By precisely asking, and answering such questions of counterfactual inference, we have the opportunity to both understand the impact of past decisions (has climate change worsened economic inequality?) and inform future choices (can we use historical electronic medical records data about decision made and outcomes, to create better protocols to enhance patient health?). In this course I will introduce some of the most common quantitative approaches to counterfactual reasoning, as well as give a wide sampling of some of the many important problems and questions that can be addressed through the lense of counterfactual reasoning, including in climate change, healthcare and economics. No prior experience with counterfactual or ¿what if¿ reasoning, nor probability, is required.

CS 41. Hap.py Code: The Python Programming Language. 2 Units.

This course is about the fundamentals and contemporary usage of the Python programming language. The primary focus is on developing best practices in writing Python and exploring the extensible and unique parts of the Python language. Topics include: Pythonic conventions, data structures such as list comprehensions, anonymous functions, iterables, powerful built-ins (e.g. map, filter, zip), and Python libraries. For the last few weeks, students will work with course staff to develop their own significant Python project. Prerequisite: CS106B, CS106X, or equivalent.

CS 43. Functional Programming Abstractions. 2 Units.

This course covers the fundamentals of functional programming and algebraic type systems, and explores a selection of related programming paradigms and current research. Haskell is taught and used throughout the course, though much of the material is applicable to other languages. Material will be covered from both theoretical and practical points of view, and topics will include higher order functions, immutable data structures, algebraic data types, type inference, lenses and optics, effect systems, concurrency and parallelism, and dependent types. Prerequisites: Programming maturity and comfort with math proofs, at the levels of CS107 and CS103.

CS 44N. Great Ideas in Graphics. 3 Units.

A hands-on interactive and fun exploration of great ideas from computer graphics. Motivated by graphics concepts, mathematical foundations and computer algorithms, students will explore an eccentric selection of "great ideas" through short weekly programming projects. Project topics will be selected from a diverse array of computer graphics concepts and historical elements.

CS 47. Cross-Platform Mobile Development. 2 Units.

The fundamentals of cross-platform mobile application development using the React Native framework (RN). Primary focus on enabling students to build apps for both iOS and Android using RN. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats. Skills developed over the course will be consolidated by the completion of a final project. No required prerequisites. Website: web.stanford.edu/class/cs47/. To enroll in the class, please fill the following application: https://forms.gle/nDnuR3R6N9LozXUdA. The application deadline is January 15th at 6:00 pm.

CS 49N. Using Bits to Control Atoms. 3 Units.

This is a crash course in how to use a stripped-down computer system about the size of a credit card (the rasberry pi computer) to control as many different sensors as we can implement in ten weeks, including LEDs, motion sensors, light controllers, and accelerometers. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. We will start with a "bare metal'' system --- no operating system, no support --- and teach you how to read device data sheets describing sensors and write the minimal code needed to control them (including how to debug when things go wrong, as they always do). This course differs from most in that it is deliberately mostly about what and why rather than how --- our hope is that the things you are able at the end will inspire you to follow the rest of the CS curriculum to understand better how things you've used work. Prerequisites: knowledge of the C programming language. A Linux or Mac laptop that you are comfortable coding on.

CS 50. Using Tech for Good. 2 Units.

Students in the class will work in small teams to implement high-impact projects for partner organizations. Taught by the CS+Social Good team, the aim of the class is to empower you to leverage technology for social good by inspiring action, facilitating collaboration, and forging pathways towards global change. Recommended: CS 106B, CS 42 or 142. Class is open to students of all years. May be repeated for credit. Cardinal Course certified by the Haas Center.

CS 51. CS + Social Good Studio: Designing Social Impact Projects. 2 Units.

Get real-world experience researching and developing your own social impact project! Students work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, community discussions, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Application required; please see cs51.stanford.edu for more information.

CS 52. CS + Social Good Studio: Implementing Social Good Projects. 2 Units.

Continuation of CS51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public. Prerequisite: CS 51, or consent of instructor.

CS 56N. Great Discoveries and Inventions in Computing. 3 Units.

This seminar will explore some of both the great discoveries that underlie computer science and the inventions that have produced the remarkable advances in computing technology. Key questions we will explore include: What is computable? How can information be securely communicated? How do computers fundamentally work? What makes computers fast? Our exploration will look both at the principles behind the discoveries and inventions, as well as the history and the people involved in those events. Some exposure to programming is required.

CS 57N. Randomness: Computational and Philosophical Approaches. 3 Units.

Is it ever reasonable to make a decision randomly? For example, would you ever let an important choice depend on the flip of a coin? Can randomness help us answer difficult questions more accurately or more efficiently? What is randomness anyway? Can an object be random? Are there genuinely random processes in the world, and if so, how can we tell? In this seminar, we will explore these questions through the lenses of philosophy and computation. By the end of the quarter students should have an appreciation of the many roles that randomness plays in both humanities and sciences, as well as a grasp of some of the key analytical tools used to study the concept. The course will be self-contained, and no prior experience with randomness/probability is necessary.
Same as: PHIL 3N

CS 58. You Say You Want a Revolution. 2 Units.

This project-based course will give creative students an opportunity to work together on revolutionary change leveraging blockchain technology. The course will provide opportunities for students to become operationally familiar with blockchain concepts, supported by presentation of blockchain fundamentals at a level accessible to those with or without a strong technical background. Specific topics include: incentives, ethics, crypto-commons, values, FOMO 3D, risks, implications and social good. Students will each discover a new possible use-case for blockchain and prototype their vision for the future accordingly. Application and impact areas may come from medicine, law, economics, history, anthropology, or other sectors. Student diversity of background will be valued highly.
Same as: Blockchain Edition

CS 58N. The Blockchain Revolution Will Not Be Televised. 3 Units.

This seminar will explore the nature of revolutions supported and enabled by technological change, using the Internet and smart phone as two historical examples and focusing on blockchain technology and potential applications such as money, banking, supply chain and market trading. In this project-based course, one meeting per week will bring in new information, including visiting experts. Other class meetings will involve team work, presentations, and discussion. Each student will help lead a section; the class collectively will produce a final book/movie/blog, in a medium selected by the class.

CS 80Q. Race and Gender in Silicon Valley. 3 Units.

Join us as we go behind the scenes of some of the big headlines about trouble in Silicon Valley. We'll start with the basic questions like who decides who gets to see themselves as "a computer person," and how do early childhood and educational experiences shape our perceptions of our relationship to technology? Then we'll see how those questions are fundamental to a wide variety of recent events from #metoo in tech companies, to the ways the under-representation of women and people of color in tech companies impacts the kinds of products that Silicon Valley brings to market. We'll see how data and the coming age of AI raise the stakes on these questions of identity and technology. How can we ensure that AI technology will help reduce bias in human decision-making in areas from marketing to criminal justice, rather than amplify it?.
Same as: AFRICAAM 80Q

CS 81SI. AI Interpretability and Fairness. 1 Unit.

As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Students have the opportunity to present a topic of interestnor application to their own projects (solo or in teams) in the final class. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. Students who will benefit most from this class have exposure to AI, such as through projects and related coursework (e.g. statistics, CS221, CS230, CS229). Students who are pursuing subjects outside of the CS department (e.g. sciences, social sciences, humanities) with sufficient mathematical maturity are welcomed to apply. Enrollment limited to 20.

CS 82SI. Wellness in Tech: Designing an Intentional Lifestyle in a Tech-Driven World. 1 Unit.

Would deleting Facebook make us all happier? Of the 16 hours we spend awake each day on average, over 11 of those hours are spent interacting with digital media. In an always-on, tech-driven world, how do we regain control over our wellbeing?nThis 1 unit course is part workshop, part seminar, with a focus on tackling and re-framing the relationship between technology and wellness. What are the principles of human flourishing, and what is technology's role in promoting them? How can self-compassion and an appreciation for diversity lead to the development of products that enhance our collective happiness? Using human-centered design thinking, we will explore how technology both propels and hinders us- as individuals and as a society. By the end of this course, you will have tangible insights and methods to regain control over your relationship with technology. No coding involved; however we will be deeply exploring the human operating system. Students from all programs and areas of study are encouraged to apply.

CS 83. Playback Theater. 3 Units.

Playback combines elements of theater, community work and storytelling. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. A playback show brings about a powerful listening and sharing experience. During the course, we will tell, listen, play together, and train in playback techniques. We will write diaries to process our experience in the context of education and research. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. In playback, as in research, we are always moving together, from the known, to the unknown, and back. There is limited enrollment for this class. Application is required.

CS 84. Emotional Intelligence. 2 Units.

This hands-on course is aimed at Stanford engineers who wish to be successful in start-ups or engineering-focused organizations. It is based on decades of observations by the instructors, witnessing that fresh graduates routinely struggle to survive and create an impact in the corporate world. A key objective is for students to develop a basic set of skills to master day-to-day personal interactions, and to understand the dynamics of work environments. The course then aims to guide students with more complex tasks, such as how to run effective meetings or how to work in multi-disciplinary teams. Whether you wish to become a start-up founder and CEO; a manager at a tech-centric company; or an individual contributor at Facebook or Google: if you wish to hit the ground running and be highly effective from your first day at work, this course is for you!.

CS 91SI. Digital Canvas: An Introduction to UI/UX Design. 2 Units.

Become familiar with prototype-design tools like Sketch and Marvel while also learning important design concepts in a low-stress environment. Focus is on the application of UI/UX design concepts to actual user interfaces: the creation of wireframes, high-fidelity mockups, and clickable prototypes. We will look at what makes a good or bad user interface, effective design techniques, and how to employ these techniques using Sketch and Marvel to make realistic prototypes. This course is ideal for anyone with little to no visual design experience who would like to build their skill set in UI/UX for app or web design. Also ideal for anyone with experience in front or back-end web development or human-computer interaction that would want to sharpen their visual design and analysis skills for UI/UX.

CS 93. Teaching AI. 1 Unit.

For graduate students who are TA-ing an AI course. This course prepares new AI section leaders to teach, write, and evaluate AI content. In class, you will be evaluating final projects individually and as a group. You will have discussions criticizing papers and assigning grades to them. You will analyze and solve discussion session problems on the board, explain algorithmsnlike backpropagation, and learn how to give constructive feedback to students. The class will also include a guest speaker who will give teaching advice and talk about AI. Focus is on teaching skills, techniques, and final projects grading. The class meets once a week for the first 6 weeks of the quarter.

CS 100A. Problem-solving Lab for CS106A. 1 Unit.

Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.

CS 100B. Problem-solving Lab for CS106B. 1 Unit.

Additional problem solving practice for the introductory CS course CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.

CS 101. Introduction to Computing Principles. 3-5 Units.

Introduces the essential ideas of computing: data representation, algorithms, programming "code", computer hardware, networking, security, and social issues. Students learn how computers work and what they can do through hands-on exercises. In particular, students will see the capabilities and weaknesses of computer systems so they are not mysterious or intimidating. Course features many small programming exercises, although no prior programming experience is assumed or required. CS101 is not a complete programming course such as CS106A. CS101 is effectively an alternative to CS105. A laptop computer is recommended for the in-class exercises.

CS 103. Mathematical Foundations of Computing. 3-5 Units.

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with CS103.

CS 103A. Mathematical Problem-solving Strategies. 1 Unit.

Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite: CS103.

CS 105. Introduction to Computers. 3-5 Units.

For non-technical majors. What computers are and how they work. Practical experience in programming. Construction of computer programs and basic design techniques. A survey of Internet technology and the basics of computer hardware. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Students with prior computer science experience at the level of 106 or above require consent of instructor. Prerequisite: minimal math skills.

CS 106A. Programming Methodology. 3-5 Units.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required.

CS 106AX. Programming Methodologies in JavaScript and Python. 3-5 Units.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. This course targets an audience with prior programming experience, and that prior experience is leveraged so material can be covered in greater depth.
Same as: Accelerated

CS 106B. Programming Abstractions. 3-5 Units.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent.

CS 106E. Exploration of Computing. 3-4 Units.

A follow up class to CS106A for non-majors which will both provide practical web programming skills and cover essential computing topics including computer security and privacy. Additional topics will include digital representation of images and music, an exploration of how the Internet works, and a look at the internals of the computer. Students taking the course for 4 units will be required to carry out supplementary programming assignments in addition to the course's regular assignments. Prerequisite: 106A or equivalent.

CS 106L. Standard C++ Programming Laboratory. 1 Unit.

Supplemental lab to 106B and 106X. Additional features of standard C++ programming practice. Possible topics include advanced C++ language features, standard libraries, STL containers and algorithms, templates, object memory management, operator overloading, and move semantics. Prerequisite: consent of instructor. Corequisite: CS106B or CS106X.

CS 106M. Enrichment Adventures in Programming Abstractions. 1 Unit.

This enrichment add-on is a companion course to CS106B to explore additional topics and go into further depth. Specific topics to be announced per-quarter. Fall quarter 2020 will focus on the algorithms that power our modern world -- search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and others. Students must be co-enrolled in CS106B. Refer to cs106m.stanford.edu for more information.

CS 106S. Coding for Social Good. 1 Unit.

Survey course on applications of fundamental computer science concepts from CS 106B/X to problems in the social good space (such as health, government, education, and environment). Each week consists of in-class activities designed by student groups, local tech companies, and nonprofits. Introduces students to JavaScript and the basics of web development. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, and storytelling through virtual reality. Pre/Corequisite: CS106B or CS106X.

CS 106X. Programming Abstractions. 3-5 Units.

Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Significant amount of additional advanced material and substantially more challenging projects. Some projects may relate to CS department research. Prerequisite: excellence in 106A or equivalent, or consent of instructor.
Same as: Accelerated

CS 107. Computer Organization and Systems. 3-5 Units.

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization. Prerequisites: 106B or X, or consent of instructor.

CS 107A. Problem-solving Lab for CS107. 1 Unit.

Additional problem solving practice for the introductory CS course CS107. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 107 required.

CS 107E. Computer Systems from the Ground Up. 3-5 Units.

Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Prerequisite: CS106B or CS106X, and consent of instructor.

CS 108. Object-Oriented Systems Design. 3-4 Units.

Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams. Prerequisite: 107.

CS 109. Introduction to Probability for Computer Scientists. 3-5 Units.

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.

CS 109A. Problem-solving Lab for CS109. 1 Unit.

Additional problem solving practice for the introductory CS course CS109. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Enrollment limited to 30 students, permission of instructor required. Concurrent enrollment in CS 109 required.

CS 110. Principles of Computer Systems. 3-5 Units.

Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities. Prerequisite: 107.

CS 110A. Problem Solving Lab for CS110. 1 Unit.

Additional design and implementation problems to complement the material taught in CS110. In-class participation is required. Prerequisite: consent of instructor. Corequisite: CS110.

CS 110L. Principles of Computer Systems Laboratory. 2 Units.

Supplemental lab to CS110. Examines how the Rust programming language can be used to build robust systems software. Course is project-based and will explore additional topics in filesystems, concurrency, and networking through the lens of Rust. Corequisite: CS110.

CS 111. Operating Systems Principles. 3-5 Units.

Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS110 that fulfills any requirement satisfied by CS110. Prerequisite: CS107.

CS 124. From Languages to Information. 3-4 Units.

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B.
Same as: LINGUIST 180, LINGUIST 280

CS 129. Applied Machine Learning. 3-4 Units.

(Previously numbered CS 229A.) You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as MATH 51.

CS 131. Computer Vision: Foundations and Applications. 3-4 Units.

Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.

CS 140. Operating Systems and Systems Programming. 3-4 Units.

Operating systems design and implementation. Basic structure; synchronization and communication mechanisms; implementation of processes, process management, scheduling, and protection; memory organization and management, including virtual memory; I/O device management, secondary storage, and file systems. Prerequisite: CS110.

CS 140E. Operating systems design and implementation. 3-4 Units.

Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.

CS 141. Introduction to Computer Sound. 3 Units.

Core mathematics and methods for computer sound with applications to computer science. Background on digital signal processing; time- and frequency-domain methods. Project-focussed exploration of computer sound areas: fundamentals of sound analysis & synthesis, robotics and learning (sound features, filterbanks & deep learning, perception, localization, tracking, manipulation), speech (recognition, synthesis), virtual and augmented reality (3D auralization, HRTFs, reverberation), computational acoustics (wave simulation, physics-based modeling, animation sound), computer music (music synthesis, instrument modeling, audio effects, historical aspects), games (game audio, music and sound design, middleware), hardware acceleration (architectures, codecs, synthesizers). Prerequisite: CS 106A or equivalent programming experience.

CS 142. Web Applications. 3 Units.

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment. Prerequisite: CS 107.

CS 143. Compilers. 3-4 Units.

Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects. Prerequisites: 103 or 103B, and 107.

CS 144. Introduction to Computer Networking. 3-4 Units.

Principles and practice. Structure and components of computer networks, with focus on the Internet. Packet switching, layering, and routing. Transport and TCP: reliable delivery over an unreliable network, flow control, congestion control. Network names, addresses and ethernet switching. Includes significant programming component in C/C++; students build portions of the internet TCP/IP software. Prerequisite: CS110.

CS 145. Data Management and Data Systems. 3-4 Units.

Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning. Prerequisites: 103 and 107 (or equivalent).

CS 146. Introduction to Game Design and Development. 3-4 Units.

This project-based course provides a survey on designing and engineering video games. Through creating their own games each week, students explore topics including 2D/3D Art, Audio, User Interface design, Production, Narrative Design, Marketing, and Publishing. Speakers from the games industry will provide insights and context during a weekly seminar. Classroom meetings will be used to foster student project discussions, and deepen understanding of material. The course culminates with students forming project teams to create a final video game. Assignments will be completed within the Unity game development engine; prior Unity experience is welcomed but not required. Given class size limitations, an online survey will be used to achieve a diverse class composition. Prerequisite: CS 106 (B or X).

CS 147. Introduction to Human-Computer Interaction Design. 3-5 Units.

Introduces fundamental methods and principles for designing, implementing, and evaluating user interfaces. Topics: user-centered design, rapid prototyping, experimentation, direct manipulation, cognitive principles, visual design, social software, software tools. Learn by doing: work with a team on a quarter-long design project, supported by lectures, readings, and studios. Prerequisite: 106B or X or equivalent programming experience. Recommended that CS Majors have also taken one of 142, 193P, or 193A.

CS 148. Introduction to Computer Graphics and Imaging. 3-4 Units.

Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. In addition to scanline rendering, ray tracing is introduced at the beginning of the course, since modern consoles now include ray tracing. This is followed by discussions of underlying mathematical concepts including triangles, normals, interpolation, texture/bump mapping, anti-aliasing, acceleration structures, etc. Importantly, the course will discuss handling light/color for image formats, computer displays, printers, etc., as well as how light interacts with the environment, constructing engineering models such as the BRDF, and various simplifications into more basic lighting and shading models. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged/rewarded. Prerequisites: CS107, MATH51.

CS 149. Parallel Computing. 3-4 Units.

This course is an introduction to parallelism and parallel programming. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, MapReduce, Apache Spark, SPMD, message passing, SIMT, transactions, and nested parallelism). Significant parallel programming assignments will be given as homework. The course is open to students who have completed the introductory CS course sequence through 110.

CS 151. Logic Programming. 3 Units.

Logic Programming is a style of programming based on symbolic logic. In writing a logic program, the programmer describes the application area of the program (as a set of logical sentences) without reference to the internal data structures or operations of the system executing the program. In this regard, a logic program is more of a specification than an implementation; and logic programs are often called runnable specifications. This course introduces basic logic programming theory, current technology, and examples of common applications, notably deductive databases, logical spreadsheets, enterprise management, computational law, and game playing. Work in the course takes the form of readings and exercises, weekly programming assignments, and a term-long project. Prerequisite: CS 106B or equivalent.

CS 152. Trust and Safety Engineering. 3 Units.

An introduction to the ways consumer internet services are abused to cause real human harm and the potential operational, product and engineering responses. Students will learn about spam, fraud, account takeovers, the use of social media by terrorists, misinformation, child exploitation, harassment, bullying and self-harm. This will include studying both the technical and sociological roots of these harms and the ways various online providers have responded. Our goal is to provide students with an understanding of how the technologies they may build have been abused in the past and how they might spot future abuses earlier. The class is taught by a long-time practitioner and supplemented by guest lecturers from tech companies and non-profits. Fulfills the Technology in Society requirement. Prerequisite: CS106B or equivalent for grad students. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material.

CS 154. Introduction to the Theory of Computation. 3-4 Units.

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.

CS 155. Computer and Network Security. 3 Units.

For seniors and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable code. Prerequisite: 110. Recommended: basic Unix.

CS 157. Computational Logic. 3 Units.

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.

CS 161. Design and Analysis of Algorithms. 3-5 Units.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.

CS 161A. Problem-Solving Lab for CS161. 1 Unit.

Additional problem solving practice for CS161. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Concurrent enrollment in CS 161 required. Limited enrollment, permission of instructor, and application required.

CS 163. The Practice of Theory Research. 3 Units.

(Previously numbered CS 353). Introduction to research in the Theory of Computing, with an emphasis on research methods (the practice of research), rather than on any particular body of knowledge. The students will participate in a highly structured research project: starting from reading research papers from a critical point of view and conducting bibliography searches, through suggesting new research directions, identifying relevant technical areas, and finally producing and communicating new insights. The course will accompany the projects with basic insights on the main ingredients of research. Research experience is not required, but basic theory knowledge and mathematical maturity are expected. The target participants are advanced undergrads as well as MS students with interest in CS theory. Prerequisites: CS161 and CS154. Limited class size.

CS 166. Data Structures. 3-4 Units.

This course is designed as a deep dive into the design, analysis, implementation, and theory of data structures. Over the course of the quarter, we'll explore fundamental techniques in data structure design (isometries, amortization, randomization, word-level parallelism, etc.). In doing so, we'll see a number of classic data structures like Fibonacci heaps and suffix trees as well as more modern data structures like count-min sketches and range minimum queries. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. Prerequisites: CS107 and CS161.

CS 168. The Modern Algorithmic Toolbox. 3-4 Units.

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

CS 170. Stanford Laptop Orchestra: Composition, Coding, and Performance. 1-5 Unit.

Classroom instantiation of the Stanford Laptop Orchestra (SLOrk) which includes public performances. An ensemble of more than 20 humans, laptops, controllers, and special speaker arrays designed to provide each computer-mediated instrument with its sonic identity and presence. Topics and activities include issues of composing for laptop orchestras, instrument design, sound synthesis, programming, and live performance. May be repeated four times for credit. Space is limited; see https://ccrma.stanford.edu/courses/128 for information about the application and enrollment process. May be repeat for credit.
Same as: MUSIC 128

CS 181. Computers, Ethics, and Public Policy. 4 Units.

Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Prerequisite: CS106A. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see https://cs181.stanford.edu for more information.

CS 181W. Computers, Ethics, and Public Policy. 4 Units.

Writing-intensive version of CS181. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, and Math/Comp Sci undergraduates. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see https://cs181.stanford.edu for more information.
Same as: WIM

CS 182. Ethics, Public Policy, and Technological Change. 5 Units.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Same as: COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182

CS 182W. Ethics, Public Policy, and Technological Change. 5 Units.

Writing-intensive version of CS182. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, and Math/Comp Sci undergraduates (and is only open to those majors). Prerequisite: CS106A. See CS182 for lecture day/time information. Enroll in either CS 182 or CS 182W, not both. Enrollment in WIM version of the course is limited to 120 students. Enrollment is restricted to seniors and coterminal students until January 4, 2021. Starting January 4, 2021, enrollment will open to all students if additional spaces remain available in the class.
Same as: WIM

CS 183E. Effective Leadership in High-Tech. 1 Unit.

You will undoubtedly leave Stanford with the technical skills to excel in your first few jobs. But non-technical skills are just as critical to making a difference. This seminar is taught by two industry veterans in engineering leadership and product management. In a small group setting, we will explore how you can be a great individual contributor (communicating with clarity, getting traction for your ideas, resolving conflict, and delivering your best work) and how you can transition into leadership roles (finding leadership opportunities, creating a great team culture, hiring and onboarding new team members). We will end by turning back to your career (picking your first job and negotiating your offer, managing your career changes, building a great network, and succeeding with mentors). Prerequisites: Preference given to seniors and co-terms in Computer Science and related majors. Enrollment limited and application required for admittance.

CS 184. Bridging Policy and Tech Through Design. 3-4 Units.

This project-based course aims to bring together students from computer science and the social sciences to work with external partner organizations at the nexus of digital technology and public policy. Students will collaborate in interdisciplinary teams on a problem with a partner organization. Along with the guidance of faculty mentors and the teaching staff, students will engage in a project with outcomes ranging from policy memos and white papers to data visualizations and software. Possible projects suggested by partner organizations will be presented at an information session in early March. Following the infosession, a course application will open for teams to be selected before the start of Spring Quarter. Students may apply to a project with a partner organization or with a preformed team and their own idea to be reviewed for approval by the course staff. There will be one meeting per week for the full class and at least one weekly meeting with the project-based team mentors. Prerequisites: Appropriate preparation depends on the nature of the project proposed, and will be verified by the teaching staff based on your application.
Same as: PUBLPOL 170

CS 187. Design for Impact in Social Systems. 3-4 Units.

The COVID pandemic has both revealed many of our underlying civilization problems and unleashed a desire for radical change. Effective responses will require people who know how to collaborate creatively and confidently, and act in systems with self-awareness. In this project based course, we will embrace complexity without being paralyzed by it. Working on a real-world challenge related to social health and civic fabric (e.g. political polarization, loneliness and social isolation) you will practice identifying high-leverage entry points for change, rigorously framing problems, and making process and product development decisions by evaluating impact. The course draws from HCD, systems thinking, strategic foresight, emotional intelligence, and agile team operations to prepare you to be even more successful as a designer, researcher, product manager, entrepreneur, or activist. If you tend to be more theory oriented, this course will get you into action. If you¿re quick to action, this course will give you a wider foundation for making a positive impact. Prerequisite: Strongly recommend CS147, ME216A or a d.school class on needfinding.

CS 190. Software Design Studio. 3-4 Units.

This course teaches the art of software design: how to decompose large complex systems into classes that can be implemented and maintained easily. Topics include the causes of complexity, modular design, techniques for creating deep classes, minimizing the complexity associated with exceptions, in-code documentation, and name selection. The class involves significant system software implementation and uses an iterative approach consisting of implementation, review, and revision. The course is taught in a studio format with in-class discussions and code reviews in addition to lectures. Prerequisite: CS 140 or equivalent. Apply at: https://web.stanford.edu/class/cs190.

CS 191. Senior Project. 1-6 Unit.

Restricted to Computer Science students. Group or individual projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Public presentation of final application or research results is required. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf.

CS 191W. Writing Intensive Senior Project. 3-6 Units.

Restricted to Computer Science students. Writing-intensive version of CS191. Register using instructor's section number. Prerequisite: Completion of at least 135 units and consent of instructor. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf.
Same as: WIM

CS 192. Programming Service Project. 1-4 Unit.

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value. Register using the section number associated with the instructor. Prerequisite: consent of instructor.

CS 193A. Android Programming. 3 Units.

Introduction to building applications for Android platform. Examines key concepts of Android programming: tool chain, application life-cycle, views, controls, intents, designing mobile UIs, networking, threading, and more. Features weekly lectures and a series of small programming projects. Phone not required, but a phone makes the projects more engaging. Prerequisites: 106B or Java experience at 106B level. Enrollment limited and application required.

CS 193C. Client-Side Internet Technologies. 3 Units.

Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax. Prerequisite: programming experience at the level of CS106A.

CS 193P. iOS Application Development. 3 Units.

Build mobile applications using tools and APIs in iOS. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. This course will require you to learn a new programming language (Swift) as well as a new-to-iOS development environment, SwiftUI. Prerequisites: All coursework (homework and final project) involves writing code, so writing a lot of code should not be ¿new¿ to you (coding experience in almost any language is valuable, but object-oriented (e.g. CS108) and/or functional programming languages (e.g. CS43) are most highly recommended). CS106A and B (or X) and CS107 (or equivalent) are hard prerequisites. Any other courses that help to develop your maturity as a programmer are also recommended.

CS 193Q. Introduction to Python Programming. 1 Unit.

CS193Q teaches basic Python programming with a similar end-condition to CS106AP: strings, lists, numbers, dicts, loops, logic, functions, testings, decomposition and style, and modules. CS193Q assumes knowledge of some programming language, and proceeds by showing how each common programming idea is expressed in Python. CS193Q moves very quickly, meeting 3 times for 4 hours for a total of 12 hours which is a mixture of lecture and lab time.

CS 193U. Video Game Development in C++ and Unreal Engine. 3 Units.

Hands-on game development in C++ using Unreal Engine 4, the game engine that triple-A games like Fortnite, PUBG, and Gears of War are all built on. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. Project-based course where you build your own games and gain a solid foundation in Unreal's architecture that will apply to any future game projects. Pre-requisites: CS106B or CS106X required. CS107 and CS110 recommended.

CS 193X. Web Programming Fundamentals. 3 Units.

Introduction to full-stack web development with an emphasis on fundamentals. Client-side topics include layout and rendering through HTML and CSS, event-driven programming through JavaScript, and single-threaded asynchronous programming techniques including Promises. Focus on modern standardized APIs and best practices. Server-side topics include the development of RESTful APIs, JSON services, and basic server-side storage techniques. Covers desktop and mobile web development. Prerequisite: 106B or equivalent.

CS 194. Software Project. 3 Units.

Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeated for credit. Prerequisites: CS 110 and CS 161.

CS 194A. Android Programming Workshop. 1 Unit.

Learn basic, foundational techniques for developing Android mobile applications and apply those toward building a single or multi page, networked Android application.

CS 194H. User Interface Design Project. 3-4 Units.

Advanced methods for designing, prototyping, and evaluating user interfaces to computing applications. Novel interface technology, advanced interface design methods, and prototyping tools. Substantial, quarter-long course project that will be presented in a public presentation. Prerequisites: CS 147, or permission of instructor.

CS 194W. Software Project. 3 Units.

Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS194. Preference given to seniors.
Same as: WIM

CS 195. Supervised Undergraduate Research. 3-4 Units.

Directed research under faculty supervision. Register using instructor's section number. Students are required to submit a written report and give a public presentation on their work. Prerequisite: consent of instructor.

CS 196. Computer Consulting. 2 Units.

Focus is on Macintosh and Windows operating system maintenance, and troubleshooting through hardware and software foundation and concepts. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. Final project. Not a programming course.
Same as: VPTL 196

CS 197. Computer Science Research. 3-4 Units.

An onramp for students interested in breaking new ground in the frontiers of computer science. Course format features faculty lectures introducing the fundamentals of computer science research, alongside special interest group meetings that provide mentorship and feedback on a real research project. CURIS students enroll for 3 units and prepare for summer research. All other students enroll for 4 units and select a research area (AI, HCI, Systems, etc.) for a quarter-long team programming project with a Ph.D. student mentor. Lecture topics include reading technical papers, practicing oral communication and technical writing skills, and independently formulating research questions. Prerequisites: In both cases, enrollment is by application. CS106B is required; CS107 is strongly recommended.

CS 198. Teaching Computer Science. 3-4 Units.

Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://cs198.stanford.edu.

CS 198B. Additional Topics in Teaching Computer Science. 1 Unit.

Students build on the teaching skills developed in CS198. Focus is on techniques used to teach topics covered in CS106B. Prerequisite: successful completion of CS198.

CS 199. Independent Work. 1-6 Unit.

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. Letter grade; if not appropriate, enroll in CS199P. Prerequisite: consent of instructor.

CS 199P. Independent Work. 1-6 Unit.

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. CR/NC only, if not appropriate, enroll in CS199. Prerequisite: consent of instructor.

CS 202. Law for Computer Science Professionals. 1 Unit.

Businesses are built on ideas. Today¿s successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. Over the past 40 years, ¿intellectual capital¿ has emerged as the leading assets class. Ocean Tomo® estimates that over 80% of the market value of S&P 500 corporations now stems from ¿intangible¿ assets, which consist largely of intellectual property (IP) assets (e.g., the company and product names, logos and designs; patentable inventions; proprietary software and databases, and other proprietary product, manufacturing and marketing information). It is therefore vital for entrepreneurs and other business professionals to have a basic understanding of IP and how it is procured, protected, and exploited. This course provides an overview of the many and varied IP issues that students will confront during their careers. It is intended to be both informative and fun. Classes will cover the basics of patent, trademark, copyright, and trade secret law. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Emerging issues concerning the federal Computer Fraud & Abuse Act (CFAA) and ¿hacking¿ will be covered, as will employment issues, including employee proprietary information and invention assignment agreements, work made for hire agreements, confidentiality agreements, non-compete agreements and other potential post-employment restrictions. Recent notable lawsuits will be discussed, including Apple v. Samsung (patents), Alice Corp. v. CLS Bank (software and business method patents), Oracle v. Google (software/APIs), Waymo v. Uber (civil and criminal trade secret theft), and hiQ v. LinkedIn (CFAA). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Guest lectures typically include experts on open source software; legal and practical issues confronted by business founders; and, consulting and testifying as an expert in IP litigation. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise.

CS 203. Cybersecurity: A Legal and Technical Perspective. 2 Units.

(Formerly IPS 251) This class will use the case method to teach basic computer, network, and information security from technology, law, policy, and business perspectives. Using real world topics, we will study the technical, legal, policy, and business aspects of an incident or issue and its potential solutions. The case studies will be organized around the following topics: vulnerability disclosure, state sponsored sabotage, corporate and government espionage, credit card theft, theft of embarrassing personal data, phishing and social engineering attacks, denial of service attacks, attacks on weak session management and URLs, security risks and benefits of cloud data storage, wiretapping on the Internet, and digital forensics. Students taking the class will learn about the techniques attackers use, applicable legal prohibitions, rights, and remedies, the policy context, and strategies in law, policy and business for managing risk. Grades will be based on class participation, two reflection papers, and a final exam. Special Instructions: This class is limited to 65 students, with an effort made to have students from Stanford Law School (30 students will be selected by lottery) and students from Computer Science (30 students) and International Policy Studies (5 students). Elements used in grading: Class Participation (20%), Written Assignments (40%), Final Exam (40%). Cross-listed with the Law School (Law 4004) and International Policy Studies (IPS course number TBD).
Same as: INTLPOL 251

CS 204. Computational Law. 2-3 Units.

Computational Law is an innovative approach to legal informatics concerned with the representation of regulations in computable form. From a practical perspective, Computational Law is important as the basis for computer systems capable of performing useful legal calculations, such as compliance checking, legal planning, and regulatory analysis. In this course, we look at the theory of Computational Law, we review relevant technology and applications, we discuss the prospects and problems of Computational Law, and we examine its philosophical and legal implications. Work in the course consists of reading, class discussion, and practical exercises.

CS 205L. Continuous Mathematical Methods with an Emphasis on Machine Learning. 3 Units.

A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. Written homework assignments focus on various concepts; additionally, students choose either a take-home final exam or a series of programming assignments geared towards neural network creation, training, and inference. (Replaces CS205A, and satisfies all similar requirements.) Prerequisites: MATH 51; Math104 or MATH113 or equivalent or comfort with the associated material.

CS 206. Exploring Computational Journalism. 3 Units.

This project-based course will explore the field of computational journalism, including the use of Data Science, Info Visualization, AI, and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech, and build trust. Please apply by Jan 15, 2021 at ecj.stanford.edu.
Same as: COMM 281

CS 208E. Great Ideas in Computer Science. 3 Units.

Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material.

CS 209. Law, Order, & Algorithms. 3 Units.

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission is by consent of instructor and is limited to 20 students. To enroll in the class, please complete the course application by March 20, available at: https://5harad.com/mse330/. Grading is based on response papers, class participation, and a final project. Prerequisite: CS 106A or equivalent knowledge of coding.
Same as: CSRE 230, MS&E 330, SOC 279

CS 210A. Software Project Experience with Corporate Partners. 3-4 Units.

Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 109 and 110.

CS 210B. Software Project Experience with Corporate Partners. 3-4 Units.

Continuation of CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 210A.

CS 213. Creating Great VR: From Ideation to Monetization. 1 Unit.

Covering everything from VR fundamentals to futurecasting to launch management, this course will expose you to best practices and guidance from VR leaders that helps positions you to build great VR experiences.

CS 217. Hardware Accelerators for Machine Learning. 3-4 Units.

This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. This course will cover classical ML algorithms such as linear regression and support vector machines as well as DNN models such as convolutional neural nets, and recurrent neural nets. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. We will cover the design of accelerators for ML model inference and training. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. To design energy-efficient accelerators, students will develop the intuition to make trade-offs between ML model parameters and hardware implementation techniques. Students will read recent research papers and complete a design project. Prerequisites: CS 149 or EE 180. CS 229 is ideal, but not required.

CS 221. Artificial Intelligence: Principles and Techniques. 3-4 Units.

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.

CS 223A. Introduction to Robotics. 3 Units.

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Same as: ME 320

CS 224N. Natural Language Processing with Deep Learning. 3-4 Units.

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Same as: LINGUIST 284, SYMSYS 195N

CS 224S. Spoken Language Processing. 2-4 Units.

Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites: CS124, CS221, CS224N, or CS229.
Same as: LINGUIST 285

CS 224U. Natural Language Understanding. 3-4 Units.

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, or CS 224S.
Same as: LINGUIST 188, LINGUIST 288, SYMSYS 195U

CS 224W. Machine Learning with Graphs. 3-4 Units.

Many complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling complex social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis. Prerequisites: CS109, any introductory course in Machine Learning.

CS 225A. Experimental Robotics. 3 Units.

Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. Prerequisites: 223A or equivalent.

CS 227B. General Game Playing. 3 Units.

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.

CS 228. Probabilistic Graphical Models: Principles and Techniques. 3-4 Units.

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.

CS 229. Machine Learning. 3-4 Units.

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/numpy, familiarity with probability theory to the equivalency of CS109 or STATS116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51.
Same as: STATS 229

CS 229M. Machine Learning Theory. 3 Units.

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. In particular, we will cover concepts and phenomenon such as uniform convergence, double descent phenomenon, implicit regularization, and problems such as matrix completion, bandits, and online learning (and generally sequential decision making under uncertainty). Prerequisites: linear algebra (MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning (CS 229, STATS 229, or STATS 315A).
Same as: STATS 214

CS 230. Deep Learning. 3-4 Units.

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.

CS 231A. Computer Vision: From 3D Reconstruction to Recognition. 3-4 Units.

(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.

CS 231C. Computer Vision and Image Analysis of Art. 3 Units.

This course presents the application of rigorous image processing, computer vision, machine learning, computer graphics and artificial intelligence techniques to problems in the history and interpretation of fine art paintings, drawings, murals and other two-dimensional works, including abstract art. The course focuses on the aspects of these problems that are unlike those addressed widely elsewhere in computer image analysis applied to physics-constrained images in photographs, videos, and medical images, such as the analysis of brushstrokes and marks, medium, inferring artists¿ working methods, compositional principles, stylometry (quantification of style), the tracing of artistic influence, and art attribution and authentication. The course revisits classic problems, such as image-based object recognition, but in highly non-realistic, stylized artworks. Recommended: One of CS 131 or EE 168 or equivalent; ARTHIST 1B. Prerequisites: Programming proficiency in at least one of C, C++, Python, Matlab or Mathematica and tools/frameworks such as OpenCV or Matlab's Image Processing toolbox.

CS 231N. Convolutional Neural Networks for Visual Recognition. 3-4 Units.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. Prerequisites: Proficiency in Python; CS131 and CS229 or equivalents; MATH21 or equivalent, linear algebra.

CS 232. Digital Image Processing. 3 Units.

Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. Recommended: EE261, EE278.
Same as: EE 368

CS 233. Geometric and Topological Data Analysis. 3 Units.

Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for graph data. Linear and non-linear dimensionality reduction techniques. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps. Networks of data sets and joint analysis for segmentation and labeling. Deep learning on irregular geometric data. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103.
Same as: CME 251

CS 234. Reinforcement Learning. 3 Units.

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Prerequisites: proficiency in python, CS 229 or equivalents or permission of the instructor; linear algebra, basic probability.

CS 235. Computational Methods for Biomedical Image Analysis and Interpretation. 3-4 Units.

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.
Same as: BIOMEDIN 260, RAD 260

CS 236. Deep Generative Models. 3 Units.

Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning. Prerequisites: Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.

CS 236G. Generative Adversarial Networks. 3 Units.

Generative Adversarial Networks (GANs) have rapidly emerged as the state-of-the-art technique in realistic image generation. This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. Their benefits and applications span realistic image editing that is omnipresent in popular app filters, enabling tumor classification under low data schemes in medicine, and visualizing realistic scenarios of climate change destruction. This course also examines key challenges of GANs today, including reliable evaluation, inherent biases, and training stability. After this course, students should be familiar with GANs and the broader generative models and machine learning contexts in which these models are situated. Prerequisites: linear algebra, statistics, CS106B, plus a graduate-level AI course such as: CS230, CS229 (or CS129), or CS221.

CS 237A. Principles of Robot Autonomy I. 3-5 Units.

Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory).
Same as: AA 174A, AA 274A, EE 260A

CS 237B. Principles of Robot Autonomy II. 3-4 Units.

This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent, CME 100 or equivalent (for linear algebra), CME 106 or equivalent (for probability theory), and AA 171/274.
Same as: AA 174B, AA 274B, EE 260B

CS 238. Decision Making under Uncertainty. 3-4 Units.

This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. Prerequisites: basic probability and fluency in a high-level programming language.
Same as: AA 228

CS 239. Advanced Topics in Sequential Decision Making. 3-4 Units.

Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites: AA 228/CS 238 or CS 221.
Same as: AA 229

CS 240. Advanced Topics in Operating Systems. 3 Units.

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.

CS 240LX. Advanced Systems Laboratory, Accelerated. 3 Units.

This is an implementation-heavy, lab-based class that covers similar topics as CS240, but by writing code versus discussing papers. Our code will run "bare-metal" (without an operating system) on the widely-used ARM-based raspberry pi. Bare-metal lets us do interesting tricks without constantly fighting a lumbering, general-purpose OS that cannot get out of its own way. We will do ten projects, one per week, where each project covers two labs of (at a minimum) several hours each and a non-trivial amount of outside work. The workload is significant, but I will aim to not waste your time. Prerequisite: CS140E or instructor permission.

CS 241. Embedded Systems Workshop. 3 Units.

Project-centric building hardware and software for embedded computing systems. Students work on an existing project of their own or join one of these projects. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, deterministic timing and synchronization, state-based programming models, filters, frequency response, and high-frequency signals, low power operation, system and PCB design, security, and networked communication. Prerequisite: CS107 (or equivalent).
Same as: EE 285

CS 242. Programming Languages. 3-4 Units.

This course explores models of computation, both old, like functional programming with the lambda calculus (circa 1930), and new, like memory-safe systems programming with Rust (circa 2010). Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, and the semantic gap between computational models and modern hardware. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the syntax, structure, and semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. In light of today's Cambrian explosion of new programming languages, this course also seeks to provide a conceptual clarity on how to compare and contrast the multitude of programming languages, models, and paradigms in the modern programming landscape. Prerequisites: 103, 110.

CS 243. Program Analysis and Optimizations. 3-4 Units.

Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection. Prerequisites: 103 or 103B, and 107.

CS 244. Advanced Topics in Networking. 3-4 Units.

Classic papers, new ideas, and research papers in networking. Architectural principles: why the Internet was designed this way? Congestion control. Wireless and mobility; software-defined networks (SDN) and network virtualization; content distribution networks; packet switching; data-center networks. Prerequisite: 144 or equivalent.

CS 244B. Distributed Systems. 3 Units.

Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites: CS 144.

CS 245. Principles of Data-Intensive Systems. 3 Units.

Most important computer applications have to reliably manage and manipulate datasets. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. Topics include storage management, query optimization, transactions, concurrency, fault recovery, and parallel processing, with a focus on the key design ideas shared across many types of data-intensive systems. Prerequisites: CS 145, 161.

CS 246. Mining Massive Data Sets. 3-4 Units.

The availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Prerequisites: At least one of CS107 or CS145.

CS 246H. Mining Massive Data Sets Hadoop Lab. 1 Unit.

Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. Students will learn how to implement data mining algorithms using Hadoop and Apache Spark, how to implement and debug complex data mining and data transformations, and how to use two of the most popular big data SQL tools. Topics: data mining, machine learning, data ingest, and data transformations using Hadoop, Spark, Apache Impala, Apache Hive, Apache Kafka, Apache Sqoop, Apache Flume, Apache Avro, and Apache Parquet. Prerequisite: CS 107 or equivalent.

CS 247A. Design for Artificial Intelligence. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking.
Same as: SYMSYS 195A

CS 247B. Design for Behavior Change. 3-4 Units.

Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS147 or equivalent.
Same as: SYMSYS 195B

CS 247G. Introduction to Game Design. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; please plan on attending every studio to take this class. nThe focus of CS247g is an introduction to theory and practice of the design of games. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. This class has multiple short projects, allowing us to cover a variety of genres, from narrative to pure strategy. Prerequisites: 147 or equivalent background.
Same as: SYMSYS 195G

CS 247I. Design for Understanding. 3-4 Units.

Complex problems require sophisticated approaches. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will create utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS 147 or equivalent.

CS 247S. Service Design. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247S is Service Design. In this course we will be looking at experiences that address the needs of multiple types of stakeholders at different touchpoints - digital, physical, and everything in between. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. Let us explore what specialized tools and processes are required to created these multi-faceted interactions. Prerequisites: CS147 or equivalent background in design thinking.
Same as: SYMSYS 195S

CS 248. Interactive Computer Graphics. 3-4 Units.

This course provides a comprehensive introduction to interactive computer graphics, focusing on fundamental concepts and techniques, as well as their cross-cutting relationship to multiple problem domains in interactive graphics (such as rendering, animation, geometry, image processing). Topics include: 2D and 3D drawing, sampling theory, interpolation, rasterization, image compositing, the real-time GPU graphics pipeline (and parallel rendering), VR rendering, geometric transformations, curves and surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, image processing, time integration, physically-based animation, and inverse kinematics. The course will involve several in-depth programming assignments and a self-selected final project that explores concepts covered in the class. Prerequisite: CS 107, MATH 51.

CS 250. Algebraic Error Correcting Codes. 3 Units.

Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say, CS109, CME106 or EE178) and "mathematical maturity" (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Same as: EE 387

CS 251. Cryptocurrencies and blockchain technologies. 3 Units.

For advanced undergraduates and for graduate students. The potential applications for Bitcoin-like technologies is enormous. The course will cover the technical aspects of cryptocurrencies, blockchain technologies, and distributed consensus. Students will learn how these systems work and how to engineer secure software that interacts with the Bitcoin network and other cryptocurrencies. Prerequisite: CS110. Recommended: CS255.

CS 252. Analysis of Boolean Functions. 3 Units.

Boolean functions are among the most basic objects of study in theoretical computer science. This course is about the study of boolean functions from a complexity-theoretic perspective, with an emphasis on analytic methods. We will cover fundamental concepts and techniques in this area, including influence and noise sensitivity, polynomial approximation, hypercontractivity, probabilistic invariance principles, and Gaussian analysis. We will see connections to various areas of theoretical computer science, including circuit complexity, pseudorandomness, classical and quantum query complexity, learning theory, and property testing. Prerequisites: CS 103 and CS 109 or equivalents. CS 154 and CS 161 recommended.

CS 253. Web Security. 3 Units.

Principles of web security. The fundamentals and state-of-the-art in web security. Attacks and countermeasures. Topics include: the browser security model, web app vulnerabilities, injection, denial-of-service, TLS attacks, privacy, fingerprinting, same-origin policy, cross site scripting, authentication, JavaScript security, emerging threats, defense-in-depth, and techniques for writing secure code. Course projects include writing security exploits, defending insecure web apps, and implementing emerging web standards. Prerequisite: CS 142 or equivalent web development experience.

CS 254. Computational Complexity. 3 Units.

An introduction to computational complexity theory. Topics include the P versus NP problem and other major challenges of complexity theory; Space complexity: Savitch's theorem and the Immerman-Szelepscényi theorem; P, NP, coNP, and the polynomial hierarchy; The power of randomness in computation; Non-uniform computation and circuit complexity; Interactive proofs. Prerequisites: 154 or equivalent; mathematical maturity.

CS 254B. Computational Complexity II. 3 Units.

A continuation of CS254 (Computational Complexity). Topics include Barriers to P versus NP; The relationship between time and space, and time-space tradeoffs for SAT; The hardness versus randomness paradigm; Average-case complexity; Fine-grained complexity; Current and new areas of complexity theory research. Prerequisite: CS254.

CS 255. Introduction to Cryptography. 3 Units.

For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications. Prerequisite: basic probability theory.

CS 257. Logic and Artificial Intelligence. 2-4 Units.

This is a course at the intersection of philosophical logic and artificial intelligence. After reviewing recent work in AI that has leveraged ideas from logic, we will slow down and study in more detail various components of high-level intelligence and the tools that have been designed to capture those components. Specific areas will include: reasoning about belief and action, causality and counterfactuals, legal and normative reasoning, natural language inference, and Turing-complete logical formalisms including (probabilistic) logic programming and lambda calculus. Our main concern will be understanding the logical tools themselves, including their formal properties and how they relate to other tools such as probability and statistics. At the end, students should expect to have learned a lot more about logic, and also to have a sense for how logic has been and can be used in AI applications. Prerequisites: A background in logic, at least at the level of PHIL 151, will be expected. In case a student is willing to put in the extra work to catch up, it may be possible to take the course with background equivalent to PHIL 150 or CS 157. A background in AI, at the level of CS 221, would also be very helpful and will at times be expected. 2 unit option only for PhD students past the second year. Course website: http://web.stanford.edu/class/cs257/.
Same as: PHIL 356C

CS 259Q. Quantum Computing. 3 Units.

The course introduces the basics of quantum algorithms, quantum computational complexity, quantum information theory, and quantum cryptography, including the models of quantum circuits and quantum Turing machines, Shor's factoring algorithms, Grover's search algorithm, the adiabatic algorithms, quantum error-correction, impossibility results for quantum algorithms, Bell's inequality, quantum information transmission, and quantum coin flipping. Prerequisites: knowledge of linear algebra, discrete probability and algorithms.

CS 260. Geometry of Polynomials in Algorithm Design. 3 Units.

Over the years, many powerful algorithms have been built via tools such as linear programming relaxations, spectral properties of graphs, and others, that all bridge the discrete and continuous worlds. This course will cover another such tool recently gaining popularity: polynomials, their roots, and their analytic properties, collectively known as geometry of polynomials. The course will cover fundamental properties of polynomials that are useful in designing algorithms, and then will showcase applications in several areas of algorithm design: combinatorial optimization, graph sparsification, high-dimensional expanders, analysis of random walks on combinatorial objects, and counting algorithms. Prerequisites: CS161 or equivalent. Basic knowledge of probability, linear algebra, and calculus.

CS 261. Optimization and Algorithmic Paradigms. 3 Units.

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty. Prerequisite: 161 or equivalent.

CS 263. Counting and Sampling. 3 Units.

This course will cover various algorithm design techniques for two intimately connected class of problems: sampling from complex probability distributions and counting combinatorial structures. A large part of the course will cover Markov Chain Monte Carlo techniques: coupling, stationary times, canonical paths, Poincare and log-Sobolev inequalities. Other topics include correlation decay in spin systems, variational techniques, holographic algorithms, and polynomial interpolation-based counting. Prerequisites: CS161 or equivalent, STAT116 or equivalent.

CS 264. Beyond Worst-Case Analysis. 3 Units.

This course is motivated by problems for which the traditional worst-case analysis of algorithms fails to differentiate meaningfully between different solutions, or recommends an intuitively "wrong" solution over the "right" one. This course studies systematically alternatives to traditional worst-case analysis that nevertheless enable rigorous and robust guarantees on the performance of an algorithm. Topics include: instance optimality; smoothed analysis; parameterized analysis and condition numbers; models of data (pseudorandomness, locality, diffuse adversaries, etc.); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Prerequisites: CS161 (required). CS261 is recommended but not required.

CS 265. Randomized Algorithms and Probabilistic Analysis. 3 Units.

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Same as: CME 309

CS 268. Geometric Algorithms. 3 Units.

Techniques for design and analysis of efficient geometric algorithms for objects in 2-, 3-, and higher dimensions. Topics: convexity, triangulations and simplicial complexes, sweeping, partitioning, and point location. Voronoi/Delaunay diagrams and their properties. Arrangements of curves and surfaces. Intersection and visibility problems. Geometric searching and optimization. Random sampling methods. Range searching. Impact of numerical issues in geometric computation. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. Prerequisite: discrete algorithms at the level of CS161. Recommended: CS164.

CS 269G. Almost Linear Time Graph Algorithms. 3 Units.

Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.
Same as: MS&E 313

CS 269I. Incentives in Computer Science. 3 Units.

Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, reputation systems, social choice, and social network analysis. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. Prerequisites: CS106B/X and CS161, or permission from the instructor.

CS 269O. Introduction to Optimization Theory. 3 Units.

Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.
Same as: MS&E 213

CS 269Q. Elements of Quantum Computer Programming. 3 Units.

For advanced undergraduates and for graduate students. Quantum computing is an emerging computational paradigm with vast potential. This course is an introduction to modern quantum programming for students who want to work with quantum computing technologies and learn about new paradigms of computation. A physics / quantum mechanics background is not required. Students will learn the model of quantum computation, quantum programming languages, hybrid quantum/classical programming, quantum algorithms, quantum error correction, and applications. The course is hands on using open source Python packages for working with publicly available quantum processors. Prerequisites: linear algebra and programming at the undergraduate level.

CS 270. Modeling Biomedical Systems. 3 Units.

At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites: CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Same as: BIOMEDIN 210

CS 271. Artificial Intelligence in Healthcare. 3-4 Units.

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare. Prerequisites: Proficiency in Python or ability to self-learn; familiarity with machine learning and basic calculus, linear algebra, statistics; familiarity with deep learning highly recommended (e.g. prior experience training a deep learning model).
Same as: BIODS 220, BIOMEDIN 220

CS 272. Introduction to Biomedical Informatics Research Methodology. 3-5 Units.

Capstone Biomedical Informatics (BMI) experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.
Same as: BIOE 212, BIOMEDIN 212, GENE 212

CS 273A. The Human Genome Source Code. 3 Units.

A computational primer to "hacking" the most amazing operating system "disk" on the planet: your genome. Handling genomic data is deceptively easy. But that's muscle. You want to be the brain, too. Topics include genome sequencing (assembling source code from code fragments); the human genome functional landscape: variable assignments (genes), control-flow logic (gene regulation) and run-time stack (epigenomics); human disease and personalized genomics (as a hunt for bugs in the human code); genome editing (code injection) to cure the incurable; and the source code modifications behind amazing animal adaptations. The course will introduce ideas from computational genomics, machine learning and natural language processing. Course includes primers on molecular biology, and text processing languages. Prerequisites: CS106A or equivalent. No biological background assumed.
Same as: BIOMEDIN 273A, DBIO 273A

CS 273B. Deep Learning in Genomics and Biomedicine. 3 Units.

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Same as: BIODS 237, BIOMEDIN 273B, GENE 236

CS 273C. Cloud Computing for Biology and Healthcare. 3 Units.

Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Same as: BIOMEDIN 222, GENE 222

CS 274. Representations and Algorithms for Computational Molecular Biology. 3-4 Units.

Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units.
Same as: BIOE 214, BIOMEDIN 214, GENE 214

CS 275. Translational Bioinformatics. 3-4 Units.

Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of CS 106A and familiarity with biology and statistics.
Same as: BIOE 217, BIOMEDIN 217, GENE 217

CS 275A. Symbolic Musical Information. 2-4 Units.

Focus on symbolic data for music applications including advanced notation systems, optical music recognition, musical data conversion, and internal structure of MIDI files.
Same as: MUSIC 253

CS 275B. Computational Music Analysis. 2-4 Units.

Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.
Same as: MUSIC 254

CS 276. Information Retrieval and Web Search. 3 Units.

Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites: CS 107, CS 109, CS 161.
Same as: LINGUIST 286

CS 278. Social Computing. 3 Units.

Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems to be effective and responsible? This course covers design patterns for social computing and crowdsourcing systems, and the foundational ideas that underpin them. Students will engage in the creation of new computationally-mediated social environments.

CS 279. Computational Biology: Structure and Organization of Biomolecules and Cells. 3 Units.

Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (CS 106A or equivalent) and an introductory course in biology or biochemistry.
Same as: BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279

CS 294A. Research Project in Artificial Intelligence. 3 Units.

Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.

CS 294S. Research Project in Software Systems and Security. 3 Units.

Topics vary. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. May be repeated for credit. Prerequisites: CS 103 and 107.

CS 294W. Writing Intensive Research Project in Computer Science. 3 Units.

Restricted to Computer Science and Computer Systems Engineering undergraduates. Students enroll in the CS 294W section attached to the CS 294 project they have chosen.

CS 298. Seminar on Teaching Introductory Computer Science. 1 Unit.

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Same as: EDUC 298

CS 300. Departmental Lecture Series. 1 Unit.

Priority given to first-year Computer Science Ph.D. students. CS Masters students admitted if space is available. Presentations by members of the department faculty, each describing informally his or her current research interests and views of computer science as a whole.

CS 309. Industrial Lectureships in Computer Science. 1 Unit.

Guest computer scientist. By arrangement. May be repeated for credit.

CS 309A. Cloud Computing Seminar. 1 Unit.

For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.

CS 315B. Parallel Computing Research Project. 3 Units.

Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project. Prerequisite: consent of instructor.

CS 316. Advanced Multi-Core Systems. 3 Units.

In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites: EE 180 (formerly 108B) and EE 282. Recommended: CS 149.

CS 319. Topics in Digital Systems. 3 Units.

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.

CS 320. Value of Data and AI. 3 Units.

Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? In this graduate seminar, we will investigate these questions by studying recent research on these topics and by hosting in-depth discussions with experts from industry and academia. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. Students will also conduct a group research projects in this field.nnPrerequisites: Sufficient mathematical maturity to follow the technical content; some familiarity with data mining and machine learning and at least an undergraduate course in statistics are recommended.

CS 323. Automated Reasoning: Theory and Applications. 3-4 Units.

Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications.

CS 325B. Data for Sustainable Development. 3-5 Units.

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. A permission code will be given to admitted students to register for the class.
Same as: EARTHSYS 162, EARTHSYS 262

CS 326. Topics in Advanced Robotic Manipulation. 3-4 Units.

This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. It includes classical concepts that are still widely used and recent approaches that have changed the way we look autonomous manipulation. We cover approaches towards motion planning and control using visual and tactile perception as well as machine learning. This course is especially concerned with new approaches for overcoming challenges in generalization from experience, exploration of the environment, and learning representation so that these methods can scale to real problems. Students are expected to present one paper in a tutorial, debate a paper once from the Pro and once from the Con side. They are also expected to propose an original research project and work on it towards a research paper. Recommended: CS 131, 223A, 229 or equivalents.

CS 327A. Advanced Robotic Manipulation. 3 Units.

Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.

CS 328. Topics in Computer Vision. 3 Units.

Fundamental issues of, and mathematical models for, computer vision. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. May be repeated for credit. Prerequisites: 205, 223B, or equivalents.

CS 329. Topics in Artificial Intelligence. 3 Units.

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.

CS 329D. Machine Learning Under Distributional Shifts. 3 Units.

The progress of machine learning systems has seemed remarkable and inexorable ¿ a wide array of benchmark tasks including image classification, speech recognition, and question answering have seen consistent and substantial accuracy gains year on year. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. The goal of the course is to introduce the variety of areas in which distributional shifts appear, as well as provide theoretical characterization and learning bounds for distribution shifts. Prerequisites: CS229 or equivalent. Recommended: CS229T (or basic knowledge of learning theory).

CS 329M. Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning. 3 Units.

This advanced graduate course explores in depth several important classes of algorithms in modern machine learning. We will focus on understanding the mathematical properties of these algorithms in order to gain deeper insights on when and why they perform well. We will also study applications of each algorithm on interesting, real-world settings. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Students will learn mathematical techniques for analyzing these algorithms and hands-on experience in using them. We will supplement the lectures with latest papers and there will be a significant research project component to the class. Prerequisites: Probability (CS 109), linear algebra (MATH 113), machine learning (CS 229), and some coding experience.

CS 329S. Machine Learning Systems Design. 3-4 Units.

This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. Students will learn about the different layers of the data pipeline, approaches to model selection, training, scaling, as well as how to deploy, monitor, and maintain ML systems. In the process, students will learn about important issues including privacy, fairness, and security. Pre-requisites: At least one of the following; CS229, CS230, CS231N, CS224N or equivalent. Students should have a good understanding of machine learning algorithms and should be familiar with at least one framework such as TensorFlow, PyTorch, JAX.

CS 330. Deep Multi-task and Meta Learning. 3 Units.

While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can benleveraged to learn more efficiently or effectively. This includes: goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster; meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly; curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer. This is a graduate-level course. By the end of the course, students should be able to understand andnimplement the state-of-the-art multi-task learning algorithms and be ready to conduct research on these topics. Prerequisites: CS 229 or equivalent. Familiarity with deep learning, reinforcement learning, and machine learning is assumed.

CS 331B. Representation Learning in Computer Vision. 3 Units.

A representation performs the task of converting an observation in the real world (e.g. an image, a recorded speech signal, a word in a sentence) into a mathematical form (e.g. a vector). This mathematical form is then used by subsequent steps (e.g. a classifier) to produce the outcome, such as classifying an image or recognizing a spoken word. Forming the proper representation for a task is an essential problem in modern AI. In this course, we focus on 1) establishing why representations matter, 2) classical and moderns methods of forming representations in Computer Vision, 3) methods of analyzing and probing representations, 4) portraying the future landscape of representations with generic and comprehensive AI/vision systems over the horizon, and finally 5) going beyond computer vision by talking about non-visual representations, such as the ones used in NLP or neuroscience. The course will heavily feature systems based on deep learning and convolutional neural networks. We will have several teaching lectures, a number of prominent external guest speakers, as well as presentations by the students on recent papers and their projects. nnRequired Prerequisites: CS131A, CS231A, CS231B, or CS231N. If you do not have the required prerequisites, please contact a member of the course staff before enrolling in this course.

CS 332. Advanced Survey of Reinforcement Learning. 3 Units.

This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic. Prerequisites: CS 221 or AA 238/CS 238 or CS 234 or CS 229 or similar experience.

CS 333. Algorithms for Interactive Robotics. 3-4 Units.

Once confined to the manufacturing floor, robots are quickly entering the public space at multiple levels: drones, surgical robots, service robots, and self-driving cars are becoming tangible technologies impacting the human experience. Our goal in this class is to learn about and design algorithms that enable robots to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on. This is a project-based graduate course that covers a broad set of algorithms in robotics, machine learning, and control theory for the goal of developing interactive human-robot systems. Recommended: Introductory course in AI, machine learning, and robotics.

CS 335. Fair, Accountable, and Transparent (FAccT) Deep Learning. 3 Units.

Deep learning-based AI systems have demonstrated remarkable learning capabilities. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAccT) of a model in addition to its performance. Although FAccT will be difficult to achieve, emerging technical approaches in this topic show promise in making better FAccT AI systems. In this course, we will study the rigorous computer science necessary foundations for FAccT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, accountability, and privacy. These topics reflect state-of-the-art research in FAccT, are socially important, and they have strong industrial interest due to government and other policy regulation. This course will focus on the algorithmic and statistical methods needed to approach FAccT AI from a deep learning perspective. We will also discuss several application areas where we can apply these techniques. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. Qualified students will have taken any one of the following, or their advanced equivalents: CS224N, CS230, CS231N, CS236, CS273B. Alternatively, students who have taken CS229 or have equivalent knowledge can be admitted with the permission of the instructors.

CS 337. AI-Assisted Care. 1 Unit.

AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.
Same as: MED 277

CS 338. Physical Human Robot Interaction. 3 Units.

Robotics researchers and futurists have long dreamed of robots that can serve as assistants or caregivers. One important research area to develop such robots in the immediate future is Physical Human-Robot Interaction (pHRI). Assistive robots have the potential to provide adaptable and intelligent assistance to people in need, but developing such a robot is challenging because the robot needs to coordinate its motion with human, often through physical contacts. Reliable mechanical and control methods need to be developed in consideration of actively participating humans, while safety and dependability issues have to be addressed to successfully introduce robots in everyday environments. In this hands-on project-based course, students will learn about future opportunities and present realities for autonomous robots that provide physical assistance to humans. Students will also gain experience with key technologies for the creation of autonomous robots, including perception, action, human-robot interaction, and learning. Prerequisites: CS223A.

CS 339N. Machine Learning Methods for Neural Data Analysis. 3 Units.

With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. This course will study statistical machine learning methods for analysing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. We will develop the theory behind these models and algorithms and then apply them to real datasets in the homeworks and final project.This course is similar to STATS215: Statistical Models in Biology and STATS366: Modern Statistics for Modern Biology, but it is specifically focused on statistical machine learning methods for neuroscience data. Prerequisites: Students should be comfortable with basic probability (STATS 116) and statistics (at the level of STATS 200). This course will place a heavy emphasis on implementing models and algorithms, so coding proficiency is required.
Same as: NBIO 220, STATS 220, STATS 320

CS 340. Topics in Computer Systems. 3-4 Units.

Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.

CS 340LX. Advanced Operating System Lab: Accelerated. 2 Units.

This is an implementation-heavy, lab-based class that continues the topics from CS240LX. The labs will be more specialized, with an emphasis on research-worthy topics and techniques. The class format will follow CS240LX: two labs, twice a week, along with a set of research papers for context. Enrollment requires instructor permission.
Same as: II

CS 341. Project in Mining Massive Data Sets. 3 Units.

Students work in teams of three to solve a problem involving the analysis of a massive dataset. A proposal, early in March is required. There will be an information session (announced in CS246) explaining the datasets available in early March and this information will also be on the CS341 course website in late February. Each accepted team will be assigned a mentor who will work with them regularly throughout the quarter. Teams will also be provided access to significant computing resources on a commercial public cloud.

CS 342. Building for Digital Health. 3 Units.

This project-based course will provide a comprehensive overview of key requirements in the design and full-stack implementation of a digital health research application. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. Student teams learn about all necessary approval processes to deploy a digital health solution (data privacy clearance/I RB approval, etc.) and be guided in the development of front-end and back-end infrastructure using best practices. The final project will be the presentation and deployment of a fully approved digital health research application. CS106A, CS106B, Recommended: CS193P/A, CS142, CS47, CS110. Limited enrollment for this course.
Same as: MED 253

CS 343D. Domain-Specific Programming Models and Compilers. 3 Units.

This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. We will study programming models from the recent literature, categorize them, and discuss their properties. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory. Prerequisites: CS161 or equivalent, STATS116 or equivalent.

CS 344. Topics in Computer Networks. 3 Units.

This class could also be called "Build an Internet Router": Students work in teams of two to build a fully functioning Internet router, gaining hands-on experience building the hardware and software of a high-performance network system. Students design the control plane in C on a linux host and design the data plane in the new P4 language on both a software switch and a high-speed hardware switch (e.g., Intel Tofino). For the midterm milestone, teams must demonstrate that their routers can interoperate with the other teams by building a small scale datacenter topology. In the final 3-4 weeks of the class, teams will participate in an open-ended design challenge. Prerequisites: At least one student in each team must have taken CS144 at Stanford and completed Lab 3 (static router). No Verilog or FPGA programming experience is required. May be repeated for credit.

CS 345S. Data-intensive Systems for the Next 1000x. 3-4 Units.

The last decade saw enormous shifts in the design of large-scale data-intensive systems due to the rise of Internet services, cloud computing, and Big Data processing. Where will we see the next 1000x increases in scale and data volume, and how should data-intensive systems accordingly evolve? This course will critically examine a range of trends, including the Internet of Things, drones, smart cities, and emerging hardware capabilities, through the lens of software systems research and design. Students will perform a comparative analysis by reading and discussing cutting-edge research while performing their own original research. Prerequisites: Strong background in software systems, especially databases (CS 245) and distributed systems (CS 244B), and/or machine learning (CS 229). Undergraduates who have completed CS 245 are strongly encouraged to attend.

CS 347. Human-Computer Interaction: Foundations and Frontiers. 3-4 Units.

(Previously numbered CS376.) How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and comment on multiple research papers per week, and perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or CS247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.

CS 348A. Computer Graphics: Geometric Modeling & Processing. 3 Units.

The mathematical tools needed for the geometrical aspects of computer graphics and especially for modeling smooth shapes. The course covers classical computer-aided design, geometry processing, and data-driven approaches for shape generation. Fundamentals: homogeneous coordinates and transformation. Theory of parametric and implicit curve and surface models: polar forms, Bézier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multi-resolution representations of geometry. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification and parametrization. Deep neural generative models for 3D geometry: parametric and implicit approaches, VAEs and GANs. Prerequisite: linear algebra at the level of CME103. Recommended: CS248.

CS 348B. Computer Graphics: Image Synthesis Techniques. 3-4 Units.

Intermediate level, emphasizing high-quality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multi-core parallelism for rendering. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.

CS 348C. Computer Graphics: Animation and Simulation. 3 Units.

Core mathematics and methods for computer animation and motion simulation. Traditional animation techniques. Physics-based simulation methods for modeling shape and motion: particle systems, constraints, rigid bodies, deformable models, collisions and contact, fluids, and fracture. Animating natural phenomena. Methods for animating virtual characters and crowds. Additional topics selected from data-driven animation methods, realism and perception, animation systems, motion control, real-time and interactive methods, and multi-sensory feedback. Recommended: CS 148 and/or 205A. Prerequisite: linear algebra.

CS 348E. Character Animation: Modeling, Simulation, and Control of Human Motion. 3 Units.

This course introduces technologies and mathematical tools for simulating, modeling, and controlling human/animal movements. Students will be exposed to integrated knowledge and techniques across computer graphics, robotics, machine learning and biomechanics. The topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, multi-body dynamics, human kinematics, muscle dynamics, trajectory optimization, learning policies for motor skills, and motion capture. Students who successfully complete this course will be able to use and modify physics simulator for character animation or robotic applications, to design/train control policies for locomotion or manipulation tasks on virtual agents, and to leverage motion capture data for synthesizing realistic virtual humans. The evaluation of this course is based on three assignments and an open-ended research project. Recommended Prerequisite: CS148 or CS205A.

CS 348I. Computer Graphics in the Era of AI. 3 Units.

This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and computational photography. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts to researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, mesh segmentation, view prediction, colorization, style transfer, sketch simplification, character animation, physics simulation, and facial animation. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, to apply neural procedural modeling for shape and scene synthesis, to implement policy learning algorithms for creating character animation, and to exploit data-driven methods for simulating physical phenomena. Recommended Prerequisites: CS248, CS231N, CS229, CS205A.

CS 348K. Visual Computing Systems. 3-4 Units.

Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Prerequisites: CS 107 or equivalent. Highly recommended: Parallel Computing (CS149) or Computer Architecture (EE 282). Students will benefit from some background in deep learning (CS 230, CS 231N), computer vision (CS 231A), digital image processing (CS 232) or computer graphics (CS248).

CS 349. Topics in Programming Systems. 3 Units.

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.

CS 349D. Cloud Computing Technology. 3 Units.