Stanford University courses of computer science department（斯坦福计算机系课程设置）_fundamental graph cut problems

斯坦福学科目前分为7个department：Business, Earth, Education, Engineering, Humanities & Sciences, Law, Medicine
Computer Science Department属于Engineering部：

下面为计算机系2021-2022的课程设置。（搬运自官网：斯坦福计算机系2021-2022年课程设置）
（note!!!：无任何盈利目的，方便自己看的同时也给需要的同学！转载请注明！）
(翻译有空再搞吧，最近没时间）

CS 1U: Practical Unix
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
Terms: Aut, Win, Spr | Units: 1
Instructors: Zelenski, J. (PI)

CS 7: Personal Finance for Engineers
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.
Terms: Aut | Units: 1
Instructors: Nash, A. (PI)

CS 9: Problem-Solving for the CS Technical Interview
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.
Terms: Aut | Units: 1
Instructors: Benson, A. (PI) ; Tullis, I. (PI)

CS 12SI: Introduction to Mobile Augmented Reality Design and Development
Over the course of 9 weeks, we’ll be covering major components of mobile AR development with Unity and AR Foundations to dig deep into concepts such as Plane Detection, Object Placement, Image and Face Tracking, Graphics, and a lot more! The class will feature student lecturers from Stanford XR leaders who have experience developing XR applications and guest speakers from industry professionals. Throughout the class, you’ll build your very own interactive AR app and share your work with others to showcase what you’ve learned. Prerequisite: CS 106A or equivalent basic coding experience.
Terms: Aut | Units: 1
Instructors: Borenstein, J. (PI)

CS 21SI: AI for Social Good
Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). The class will focus on techniques from machine learning and deep learning, including regression, 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 MATH51, comfort with probability at the level of CS109 (or equivalent). Application required for enrollment.
Terms: Spr | Units: 2
Instructors: Piech, C. (PI)

CS 22A: The Social & Economic Impact of Artificial Intelligence (INTLPOL 200)
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.
Terms: Win | Units: 1
Instructors: Kaplan, J. (PI)

CS 24: Minds and Machines (LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200)
(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.
Terms: Aut, Win | Units: 4 | UG Reqs: WAY-FR, GER:DB-SocSci
Instructors: Goodman, N. (PI) ; Lassiter, D. (PI) ; Yoon, E. (PI)

CS 25: Transformers United
Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs), Speech or even Biology. Among other things, transformers have enabled the creation of powerful language models like GPT 3 and were instrumental in DeepMind’s recent Alphafold2, that tackles protein folding. In this seminar, we examine the details of how transformers work, and dive deep into the different kinds of transformers and how they’re applied in different fields. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. We will invite people at the forefront of transformers research across different domains for guest lectures. Prerequisites: Basic knowledge of Deep Learning (must understand attention) or CS224N/ CS231N/ CS230. To apply, fill out this form: https://forms.gle/TzAtqjZ4vnjhNMSy7
Terms: Aut | Units: 1
Instructors: Manning, C. (PI)

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

CS 41: Hap.py Code: The Python Programming Language
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.
Terms: Spr | Units: 2
Instructors: Cain, J. (PI)

CS 44N: Great Ideas in Graphics
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.
Terms: Aut | Units: 3
Instructors: James, D. (PI)

CS 46N: Data-Driven Decisions and Discovery
The use of data to drive decisions and discoveries has increased dramatically over the past two decades, thanks to prevalent data collection, cheaper storage, faster computers, and sophisticated new algorithms. This introductory seminar will have three interwoven components: (1) Hands-on instruction in tools and techniques for working with data, from spreadsheets to data visualization systems to machine learning packages. (2) A suite of case studies where data has been key to decision-making or discovery, drawn from a wide variety of domains. (3) Ethical issues including privacy in data collection and use, the effect of bias in data-driven decision making, and evaluating claims about data-driven results and recommendations. Students will be expected to complete short assignments with data tools, a larger project on a dataset of personal interest, and a short case study presentation. No computer programming experience is required.
Terms: Spr | Units: 3
Instructors: Widom, J. (PI)

CS 47: Cross-Platform Mobile Development
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.
Terms: Win | Units: 2
Instructors: Landay, J. (PI)

CS 49N: Using Bits to Control Atoms
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.
Terms: Aut, Sum | Units: 3
Instructors: Engler, D. (PI)

CS 51: CS + Social Good Studio: Designing Social Impact Projects
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.
Terms: Win | Units: 2
Instructors: Cain, J. (PI)

CS 52: CS + Social Good Studio: Implementing Social Good Projects
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.
Terms: Spr | Units: 2
Instructors: Cain, J. (PI)

CS 56N: Great Discoveries and Inventions in Computing
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.
Terms: Win | Units: 3
Instructors: Hennessy, J. (PI)

CS 57N: Randomness: Computational and Philosophical Approaches (PHIL 3N)
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.
Terms: Win | Units: 3
Instructors: Icard, T. (PI) ; Wootters, M. (PI)

CS 59SI: Quantum Computing: Open-Source Project Experience
This course focuses on giving quantum software engineering industry experience with open-source projects proposed by frontier quantum computing and quantum device corporate partners.Quantum computing and quantum information industry sponsors submit open-source projects for students or teams of students to build and create solutions throughout the quarter with mentorship from the company. Gain experience with quantum mechanics, quantum computing, and real-worldnnsoftware development. Prerequisites: Computer science basics (106A, 106B), some undergraduate physics and basic understanding of quantum computing (no formal coursework in quantum computing required)
Terms: Spr | Units: 2
Instructors: Bouland, A. (PI)

CS 80Q: Race and Gender in Silicon Valley (AFRICAAM 80Q)
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?
Terms: Aut | Units: 3 | UG Reqs: WAY-ED
Instructors: Lee, C. (PI)

CS 83: Playback Theater
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.
Terms: Win | Units: 3 | UG Reqs: WAY-CE
Instructors: Reingold, O. (PI)

CS 100A: Problem-solving Lab for CS106A
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Parlante, N. (PI) ; Sahami, M. (PI) ; Zeng, B. (TA)

CS 100B: Problem-solving Lab for CS106B
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Gregg, C. (PI) ; Lee, C. (PI) ; Schwarz, K. (PI) …

CS 103: Mathematical Foundations of Computing
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.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-FR
Instructors: Aiken, A. (PI) ; Lee, C. (PI) ; Liu, A. (PI) …

CS 103A: Mathematical Problem-solving Strategies
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.
Terms: Win, Spr | Units: 1
Instructors: Lee, C. (PI)

CS 105: Introduction to Computers
For non-technical majors. What computers are and how they work. Practical experience in development of websites and an introduction to programming. 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.
Terms: Aut, Spr | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Young, P. (PI) ; Bravo, M. (TA) ; Wang, N. (TA)

CS 106A: Programming Methodology
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.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: WAY-FR, GER:DB-EngrAppSci
Instructors: Parlante, N. (PI) ; Sahami, M. (PI) ; Woodrow, J. (TA)

CS 106B: Programming Abstractions
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.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Gregg, C. (PI) ; Lee, C. (PI) ; Schwarz, K. (PI) …

CS 106E: Exploration of Computing
This course, designed for the non-computer scientist, will provide students with a solid foundation in the concepts and terminology behind computers, the Internet, and software development. It will give you better understanding and insight when working with technology. It will be particularly useful to future managers and PMs who will work with or who will lead programmers and other tech workers. But it will be useful to anyone who wants a better understanding of tech concepts and terms. We’ll start by covering the foundations of Computer Hardware, the CPU, Operating Systems, Computer Networks, and the Web. We will then use our foundation to explore a variety of tech-related topics including Computer Security (how computers are attacked and defensive measures that can be taken); Cloud Computing, Artificial Intelligence, Software Development, Human-Computer Interaction, and Computer Theory.nnPrerequisites: Some programming experience at the High School level of above will help students get the most out of the class, but the course can be successfully completed with no prerequisites.
Terms: Spr | Units: 3
Instructors: Young, P. (PI)

CS 106L: Standard C++ Programming Laboratory
This class explores features of the C++ programming language beyond what’s covered in CS106B. Topics include core C++ language features (e.g. const-correctness, operator overloading, templates, move semantics, and lambda expressions) and standard libraries (e.g. containers, algorithms, and smart pointers). Pre- or corequisite: CS106B or equivalent. Prerequisite: CS106B or equivalent. CS106L may be taken concurrently with CS106B.
Terms: Aut, Win, Spr | Units: 1
Instructors: Schwarz, K. (PI)

CS 106M: Enrichment Adventures in Programming Abstractions
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; past topics have included search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and numerical recipes. Students must be co-enrolled in CS106B. Refer to cs106m.stanford.edu for more information.
Terms: Aut | Units: 1
Instructors: Chang, M. (PI) ; Zelenski, J. (PI)

CS 106S: Coding for Social Good
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Cain, J. (PI)

CS 107: Computer Organization and Systems
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.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: WAY-FR, GER:DB-EngrAppSci
Instructors: Gregg, C. (PI) ; Troccoli, N. (PI) ; Benson, A. (TA) …

CS 107A: Problem-solving Lab for CS107
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Gregg, C. (PI) ; Troccoli, N. (PI) ; Benson, A. (TA)

CS 107E: Computer Systems from the Ground Up
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. Enrollment limited to 40. Check website for details: http://cs107e.stanford.edu on student selection process. Prerequisite: CS106B or CS106X, and consent of instructor. There is a $75 course lab fee.
Terms: Aut, Win, Spr | Units: 3-5 | UG Reqs: WAY-FR
Instructors: Gregg, C. (PI) ; Hanrahan, P. (PI) ; Kozyrakis, C. (PI) …

CS 108: Object-Oriented Systems Design
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.
Terms: Win | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Young, P. (PI)

CS 109: Introduction to Probability for Computer Scientists
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.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors: Arthurs, N. (PI) ; Cain, J. (PI) ; Piech, C. (PI) …

CS 109A: Problem-solving Lab for CS109
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Cain, J. (PI) ; Piech, C. (PI) ; da Silva Sampaio, G. (TA)

CS 110: Principles of Computer Systems
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.
Terms: Aut, Win | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci
Instructors: Cain, J. (PI) ; Troccoli, N. (PI) ; DeMichele, P. (TA) …

CS 110A: Problem Solving Lab for CS110
Additional design and implementation problems to complement the material taught in CS110. In-class participation is required. Prerequisite: consent of instructor. Corequisite: CS110.
Terms: Aut, Win | Units: 1
Instructors: Cain, J. (PI) ; Troccoli, N. (PI) ; Kaur, A. (TA)

CS 110L: Safety in Systems Programming
Supplemental lab to CS 110. Explores how program analysis tools can find common bugs in programs and demonstrates how we can use the Rust programming language to build robust systems software. Course is project-based and will examine additional topics in concurrency and networking through the lens of Rust. Corequisite: CS 110
Terms: Aut, Win | Units: 2
Instructors: Rossman, T. (PI)

CS 111: Operating Systems Principles
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.
Terms: Spr | Units: 3-5
Instructors: Cain, J. (PI) ; Ousterhout, J. (PI)

CS 112: Operating systems kernel implementation project
Students will learn the details of how operating systems work throughnfour implementation projects in the Pintos operating system. Thenprojects center around threads, processes, virtual memory, and filensystems. This class should not be taken by students who have taken ornplan to take CS212 or CS140. Prerequisite: CS111 or permission of theninstructor.
Terms: Win | Units: 3
Instructors: Mazieres, D. (PI)

CS 114: Selected Reading of Computer Science Research
Detailed reading of 5-10 research publications in computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to discuss the strengths and weaknesses of the work. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each piece of work includes a guest lecture by one of its authors.
Terms: Spr | Units: 3
Instructors: Levis, P. (PI)

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
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
Terms: Aut | Units: 3-4 | UG Reqs: WAY-AQR
Instructors: Jurafsky, D. (PI) ; Cruz, D. (TA) ; Kim, M. (TA) …

CS 129: Applied Machine Learning
(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.
Terms: Spr | Units: 3-4
Instructors: Ng, A. (PI)

CS 131: Computer Vision: Foundations and Applications
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.
Terms: Aut | Units: 3-4
Instructors: Gaidon, A. (PI) ; Niebles Duque, J. (PI) ; Moore, A. (TA) …

CS 140: Operating Systems and Systems Programming
Covers key concepts in computer systems through the lens of operatingnsystem design and implementation. Topics include threads, scheduling,nprocesses, virtual memory, synchronization, multi-core architectures,nmemory consistency, hardware atomics, memory allocators, linking, I/O,nfile systems, and virtual machines. Concepts are reinforced with fournkernel programming projects in the Pintos operating system. This classnmay be taken as an accelerated single-class alternative to the CS111,nCS112 sequence; conversely, the class should not be taken by studentsnwho have already taken CS111 or CS112
Terms: Win | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci
Instructors: Mazieres, D. (PI)

CS 140E: Operating systems design and implementation
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.
Terms: Win | Units: 3-4
Instructors: Engler, D. (PI)

CS 142: Web Applications
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.
Terms: Win, Spr | Units: 3
Instructors: Rosenblum, M. (PI)

CS 143: Compilers
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, 107 equivalent, or consent from instructor.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Kjoelstad, F. (PI)

CS 144: Introduction to Computer Networking
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.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Winstein, K. (PI) ; Bell, T. (TA) ; Raghavan, D. (TA) …

CS 145: Data Management and Data Systems
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).
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Shivakumar, N. (PI) ; Bansal, A. (TA) ; Gong, S. (TA) …

CS 147: Introduction to Human-Computer Interaction Design
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.nnPlease note: Less than 5 is only allowed for graduate students.
Terms: Win | Units: 3-5
Instructors: Landay, J. (PI)

CS 148: Introduction to Computer Graphics and Imaging
This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer generated images. The beginning of the course focuses on using Blender to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Then we move on to a more fundamental understanding of light and color, as well as how it impacts computer displays and printers. From this we discuss more thoroughly how light interacts with the environment, and we construct engineering models such as the BRDF and discuss various simplifications into more basic lighting and shading models. Finally, we discuss ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras in order to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class project consists of building out a ray tracer to create a visually compelling image. Starter codes and code bits will be provided here and there 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 towards in person “demos” of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Prerequisites: CS 107, MATH 51.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-CE
Instructors: Fedkiw, R. (PI) ; Hong, K. (TA) ; Iswara, A. (TA) …

CS 149: Parallel Computing
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.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Fatahalian, K. (PI) ; Olukotun, O. (PI) ; Deng, Y. (TA) …

CS 151: Logic Programming
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.
Terms: Spr | Units: 3
Instructors: Genesereth, M. (PI)

CS 152: Trust and Safety Engineering
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.
Terms: Win | Units: 3
Instructors: Stamos, A. (PI)

CS 154: Introduction to the Theory of Computation
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.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci
Instructors: Reingold, O. (PI) ; Cao, R. (TA) ; Di, C. (TA) …

CS 155: Computer and Network Security
For juniors, 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 software. Prerequisite: 110. Recommended: basic Unix.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci
Instructors: Boneh, D. (PI)

CS 157: Computational Logic
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.
Terms: Aut | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Genesereth, M. (PI) ; Akrami, K. (TA) ; Dahl, A. (TA) ; Fahmy, H. (TA)

CS 161: Design and Analysis of Algorithms
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, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching, amortized analysis, stable matchings and approximation algorithms. Prerequisite: 103 or 103B; 109 or STATS 116.
Terms: Aut, Win, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Anari, N. (PI) ; Charikar, M. (PI) ; Hulett, R. (PI) …

CS 161A: Problem-Solving Lab for CS161
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.
Terms: Aut, Win | Units: 1
Instructors: Anari, N. (PI) ; Charikar, M. (PI) ; Rubinstein, A. (PI) ; Wang, A. (TA)

CS 163: The Practice of Theory Research
(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.
Terms: Win | Units: 3 | UG Reqs: WAY-SMA
Instructors: Reingold, O. (PI)

CS 166: Data Structures
This course is a deep dive into the design, analysis, implementation,nand theory of data structures. Over the course of the quarter, we’llnexplore fundamental techniques in data structure design (isometries,namortization, randomization, etc.) and explore perspectives andnintuitions useful for developing new data structures. We’ll do so bynsurveying classic data structures like Fibonacci heaps and suffix trees,nas well as more modern data structures like count-min sketches and rangenminimum queries. By the time we’ve finished, we’ll have seen some trulynbeautiful strategies for solving problems efficiently. Prerequisites:nCS107 and CS161.
Terms: Spr | Units: 3-4
Instructors: Schwarz, K. (PI)

CS 168: The Modern Algorithmic Toolbox
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.
Terms: Spr | Units: 3-4
Instructors: Valiant, G. (PI)

CS 170: Stanford Laptop Orchestra: Composition, Coding, and Performance (MUSIC 128)
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
Terms: Spr | Units: 1-5 | UG Reqs: WAY-CE | Repeatable 4 times (up to 20 units total)
Instructors: Wang, G. (PI) ; Mulshine, M. (TA)

CS 182: Ethics, Public Policy, and Technological Change (COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182)
Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER
Instructors: Reich, R. (PI) ; Sahami, M. (PI) ; Weinstein, J. (PI)

CS 182W: Ethics, Public Policy, and Technological Change (WIM)
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.nEnrollment is restricted to seniors and coterminal students until January 3,2022. Starting January 3, 2022, enrollment will open to all students if additional spaces remain available in the class.
Terms: Win | Units: 5 | UG Reqs: WAY-ER
Instructors: Reich, R. (PI) ; Sahami, M. (PI) ; Weinstein, J. (PI)

CS 183E: Effective Leadership in High-Tech
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.
Terms: Aut | Units: 1
Instructors: Finley, M. (PI) ; Goldfein, J. (PI)

CS 184: Bridging Policy and Tech Through Design (PUBLPOL 170)
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.
Terms: Aut, Spr | Units: 3-4
Instructors: Goel, A. (PI)

CS 190: Software Design Studio
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
Terms: Win | Units: 3-4
Instructors: Ousterhout, J. (PI)

CS 191: Senior Project
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
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 191W: Writing Intensive Senior Project (WIM)
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
Terms: Aut, Win, Spr | Units: 3-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 192: Programming Service Project
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.
Terms: Aut, Win, Spr, Sum | Units: 1-4 | Repeatable for credit
Instructors: Achour, S. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) …

CS 193C: Client-Side Internet Technologies
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.
Terms: Sum | Units: 3
Instructors: Young, P. (PI) ; Cornn, K. (TA) ; Erdman, K. (TA) ; Weiss, P. (TA)

CS 193Q: Introduction to Python Programming
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.
Terms: Aut | Units: 1
Instructors: Parlante, N. (PI)

CS 193X: Web Programming Fundamentals
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.
Terms: Win | Units: 3
Instructors: Chang, M. (PI)

CS 194: Software Project
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: CS109 and CS161.
Terms: Win, Spr | Units: 3 | Repeatable for credit
Instructors: Borenstein, J. (PI)

CS 194A: Android Programming Workshop
Learn basic, foundational techniques for developing Android mobile applications and apply those toward building a single or multi page, networked Android application.
Terms: Aut | Units: 1
Instructors: Borenstein, J. (PI) ; Pandey, R. (PI)

CS 194W: Software Project (WIM)
Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS194. Preference given to seniors. Prerequisites: CS109 and CS161.
Terms: Win, Spr | Units: 3
Instructors: Borenstein, J. (PI)

CS 195: Supervised Undergraduate Research
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.
Terms: Aut, Win, Spr, Sum | Units: 3-4 | Repeatable 20 times (up to 100 units total)
Instructors: Achour, S. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) …

CS 197: Computer Science Research
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.
Terms: Aut | Units: 3-4
Instructors: Lee, C. (PI) ; Tamkin, A. (PI) ; Cochran, K. (TA) ; Haghighi, N. (TA)

CS 198: Teaching Computer Science
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.
Terms: Aut, Win, Spr | Units: 3-4
Instructors: Sahami, M. (PI) ; Eng, K. (TA) ; Lee, E. (TA) ; Master, T. (TA)

CS 198B: Additional Topics in Teaching Computer Science
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.
Terms: Aut, Win, Spr | Units: 1
Instructors: Eng, K. (PI) ; Lee, E. (PI) ; Master, T. (PI) ; Sahami, M. (PI)

CS 199: Independent Work
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.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 199P: Independent Work
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.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 202: Law for Computer Science Professionals
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.
Terms: Aut, Spr | Units: 1
Instructors: Hansen, D. (PI)

CS 204: Computational Law
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.
Terms: Spr | Units: 2-3
Instructors: Genesereth, M. (PI)

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning
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 and (straightforward) quizzes focus on various concepts; additionally, students can opt in to 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.
Terms: Win | Units: 3
Instructors: Fedkiw, R. (PI)

CS 206: Exploring Computational Journalism (COMM 281)
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
Terms: Win | Units: 3
Instructors: Agrawala, M. (PI) ; Tumgoren, S. (PI)

CS 208E: Great Ideas in Computer Science
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.
Terms: Aut | Units: 3
Instructors: Gregg, C. (PI) ; Banerjee, G. (TA)

CS 210A: Software Project Experience with Corporate Partners
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: CS109 and CS161.
Terms: Win | Units: 3-4
Instructors: Borenstein, J. (PI)

CS 210B: Software Project Experience with Corporate Partners
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
Terms: Spr | Units: 3-4
Instructors: Borenstein, J. (PI)

CS 212: Operating Systems and Systems Programming
Covers key concepts in computer systems through the lens of operatingnsystem design and implementation. Topics include threads, scheduling,nprocesses, virtual memory, synchronization, multi-core architectures,nmemory consistency, hardware atomics, memory allocators, linking, I/O,nfile systems, and virtual machines. Concepts are reinforced with fournkernel programming projects in the Pintos operating system. This classnmay be taken as an accelerated single-class alternative to the CS111,nCS112 sequence; conversely, the class should not be taken by studentsnwho have already taken CS111 or CS112.
Terms: Win | Units: 3-5
Instructors: Mazieres, D. (PI)

CS 214: Selected Reading of Computer Science Research
Detailed reading of 5-10 research publications in computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to discuss the strengths and weaknesses of the work. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each piece of work includes a guest lecture by one of its authors.
Terms: Spr | Units: 3
Instructors: Levis, P. (PI)

CS 221: Artificial Intelligence: Principles and Techniques
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.
Terms: Aut, Spr | Units: 3-4
Instructors: Hashimoto, T. (PI) ; Liang, P. (PI) ; Sadigh, D. (PI) …

CS 223A: Introduction to Robotics (ME 320)
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.
Terms: Win | Units: 3
Instructors: Ganguly, S. (PI) ; Khatib, O. (PI)

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284, SYMSYS 195N)
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.
Terms: Win | Units: 3-4
Instructors: Manning, C. (PI)

CS 224S: Spoken Language Processing (LINGUIST 285)
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.
Terms: Spr | Units: 2-4
Instructors: Maas, A. (PI)

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288, SYMSYS 195U)
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.
Terms: Spr | Units: 3-4
Instructors: Potts, C. (PI)

CS 224V: Conversational Virtual Assistants with Deep Learning
While commercial virtual assistants today can perform over hundreds of thousands of skills, they require a tremendous amount of manual labor. This course focuses on the latest virtual assistant research that uses deep learning to lower the development cost, improve the scalability and robustness, and to add dialogue capabilities to enhance the user experience. Students will learn both the theory and practice with written and programming assignments, as well as a course project of their own design. Topics include: a virtual assistant architecture that uses deep learning to (1) semantically parse dialogues to the ThingTalk virtual assistant programming language, (2) generate responses, and (3) recover from parsing errors through user feedback; neural dialogue semantic parser generators from high-level specifications such as database schemas and API signatures; robust, sample-efficient training for dialogues by combining few-shot data with synthesized data; multilingual, mixed-initiative, multimodal assistants; federated privacy-protecting assistants. nnPrerequisites: one of LINGUIST 180/280, CS 124, CS 224N, CS 224S, 224U.
Terms: Aut | Units: 3-4
Instructors: Lam, M. (PI) ; Chi, E. (TA) ; Mundada, S. (TA)

CS 224W: Machine Learning with Graphs
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.
Terms: Aut | Units: 3-4
Instructors: Leskovec, J. (PI) ; Chang, S. (TA) ; Chen, X. (TA) …

CS 225A: Experimental Robotics
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.
Terms: Spr | Units: 3
Instructors: Khatib, O. (PI)

CS 228: Probabilistic Graphical Models: Principles and Techniques
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.
Terms: Win | Units: 3-4
Instructors: Ermon, S. (PI)

CS 229: Machine Learning (STATS 229)
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 to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205.
Terms: Aut, Spr, Sum | Units: 3-4
Instructors: Avati, A. (PI) ; Charikar, M. (PI) ; Ma, T. (PI) …

CS 229M: Machine Learning Theory (STATS 214)
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, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. nnPrerequisites: 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).
Terms: Aut | Units: 3
Instructors: Ma, T. (PI) ; Cherian, J. (TA) ; Ray, S. (TA) …

CS 230: Deep Learning
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.
Terms: Aut, Spr | Units: 3-4 | UG Reqs: WAY-AQR, WAY-FR
Instructors: Katanforoosh, K. (PI) ; Ng, A. (PI) ; Chen, Y. (TA) …

CS 231A: Computer Vision: From 3D Reconstruction to Recognition
(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.
Terms: Win | Units: 3-4
Instructors: Bohg, J. (PI)

CS 231N: Deep Learning for Computer Vision
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.
Terms: Spr | Units: 3-4
Instructors: Gao, R. (PI) ; Li, F. (PI) ; Wu, J. (PI)

CS 233: Geometric and Topological Data Analysis (CME 251)
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. Linear and non-linear dimensionality reduction techniques. Graph representations of data and spectral methods. The rudiments of computational topology and persistent homology on sampled spaces, with applications. Global and local geometry descriptors allowing for various kinds of invariances. Alignment, matching, and map/correspondence computation between geometric data sets. Annotation tools for geometric data. Geometric deep learning on graphs and sets. Function spaces and functional maps. Networks of data sets and joint learning for segmentation and labeling. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103.
Terms: Spr | Units: 3
Instructors: Guibas, L. (PI)

CS 234: Reinforcement Learning
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.
Terms: Win | Units: 3
Instructors: Brunskill, E. (PI)

CS 235: Computational Methods for Biomedical Image Analysis and Interpretation (BIOMEDIN 260, RAD 260)
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.
Terms: Spr | Units: 3-4
Instructors: Rubin, D. (PI)

CS 236: Deep Generative Models
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.
Terms: Aut | Units: 3
Instructors: Ermon, S. (PI) ; Song, Y. (PI) ; He, K. (TA) …

CS 236G: Generative Adversarial Networks
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.
Terms: Win | Units: 3
Instructors: Zhou, S. (PI)

CS 237A: Principles of Robot Autonomy I (AA 174A, AA 274A, EE 160A, EE 260A)
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).
Terms: Aut | Units: 3-4
Instructors: Schmerling, E. (PI) ; Banerjee, S. (TA) ; Brown, R. (TA) …

CS 237B: Principles of Robot Autonomy II (AA 174B, AA 274B, EE 260B)
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.
Terms: Win | Units: 3-4
Instructors: Bohg, J. (PI) ; Pavone, M. (PI) ; Sadigh, D. (PI)

CS 238: Decision Making under Uncertainty (AA 228)
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.
Terms: Aut | Units: 3-4
Instructors: Kochenderfer, M. (PI) ; Betterton, J. (TA) ; Brown, J. (TA) …

CS 239: Advanced Topics in Sequential Decision Making (AA 229)
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.
Terms: Win | Units: 3-4
Instructors: Kochenderfer, M. (PI)

CS 240: Advanced Topics in Operating Systems
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.
Terms: Spr | Units: 3 | Repeatable for credit
Instructors: Engler, D. (PI)

CS 240LX: Advanced Systems Laboratory, Accelerated
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.
Terms: Spr | Units: 3
Instructors: Engler, D. (PI)

CS 241: Embedded Systems Workshop (EE 285)
Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of CS107, EE101A, EE108, ME80.
Terms: Aut, Spr | Units: 3 | Repeatable 3 times (up to 9 units total)
Instructors: Levis, P. (PI)

CS 242: Programming Languages
This course explores foundational models of computation, such as the lambda calculus and other small calculi, and the incorporation of basic advances in PL theory into modern programming languages such as Haskell and Rust. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, verification, 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 semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. Prerequisites: 103, 110.
Terms: Aut | Units: 3-4
Instructors: Aiken, A. (PI) ; Chirananthavat, T. (TA) ; Han, Z. (TA) …

CS 243: Program Analysis and Optimizations
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.
Terms: Spr | Units: 3-4

CS 244: Advanced Topics in Networking
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.
Terms: Spr | Units: 3-4
Instructors: Kim, C. (PI)

CS 244B: Distributed Systems
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.
Terms: Spr | Units: 3
Instructors: Mazieres, D. (PI)

CS 245: Principles of Data-Intensive Systems
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.
Terms: Win | Units: 3-4
Instructors: Zaharia, M. (PI)

CS 246: Mining Massive Data Sets
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.
Terms: Win | Units: 3-4 | UG Reqs: WAY-FR
Instructors: Leskovec, J. (PI)

CS 247A: Design for Artificial Intelligence (SYMSYS 195A)
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.
Terms: Spr | Units: 3-4

CS 247B: Design for Behavior Change (SYMSYS 195B)
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.
Terms: Win | Units: 3-4
Instructors: Wodtke, C. (PI)

CS 247G: Design for Play (SYMSYS 195G)
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. The focus of CS247g is an introduction to theory and practice of game design. 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.
Terms: Spr | Units: 3-4
Instructors: Wodtke, C. (PI)

CS 247I: Design for Understanding
Complex problems require nuanced design approaches. In this project-based hands-on course, students explore the design of systems, information and interface for human use. Each quarter we pick a different challenging topic to explore and explain; past classes have included fake news, electoral politics and gender. Students will create an explainer, an information site and a game as a response to the challenges presented. We will model the flow of interactions, data and context, and craft a design that is useful, appropriate and robust. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS 147 or equivalent.
Terms: Aut | Units: 3-4
Instructors: Wodtke, C. (PI) ; Barnett, B. (TA) ; Inouye, K. (TA) ; Verma, M. (TA)

CS 247S: Service Design (SYMSYS 195S)
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.nnNote: You must sign up for both sections of CS247 so that the class runs from 9:45 - 1:15 on Wednesday and Friday for all students.
Terms: Win | Units: 3-4
Instructors: Stanford, J. (PI)

CS 248: Interactive Computer Graphics
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.
Terms: Win | Units: 3-4
Instructors: Fatahalian, K. (PI) ; James, D. (PI)

CS 249I: The Modern Internet
Advanced networking course that covers how the Internet has evolved and operates today. Topics include modern Internet topology and routing practices, recently introduced network protocols, popular content delivery strategies, and pressing privacy, security, and abuse challenges. The course consists of a mixture of lecture, guest talks, and investigative projects where students will analyze how Internet operates in practice. Prerequisite: CS 144, EE 284, or equivalent.
Terms: Aut | Units: 3
Instructors: Durumeric, Z. (PI) ; Izhikevich, L. (TA)

CS 250: Algebraic Error Correcting Codes (EE 387)
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.
Terms: Win | Units: 3
Instructors: Wootters, M. (PI)

CS 251: Cryptocurrencies and blockchain technologies
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 Blockchains like Bitcoin, Ethereum, and others. Prerequisite: CS110. Recommended: CS255.
Terms: Aut | Units: 3
Instructors: Boneh, D. (PI) ; Hogan, M. (TA) ; Kam, E. (TA) …

CS 253: Web Security
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.
Terms: Aut | Units: 3
Instructors: Aboukhadijeh, F. (PI) ; Estrada-Arias, D. (TA) ; Gu, T. (TA) ; Zeng, A. (TA)

CS 254: Computational Complexity
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.
Terms: Win | Units: 3
Instructors: Tan, L. (PI)

CS 254B: Computational Complexity II
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.
Terms: Spr | Units: 3
Instructors: Tan, L. (PI)

CS 255: Introduction to Cryptography
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.
Terms: Win | Units: 3
Instructors: Boneh, D. (PI)

CS 259Q: Quantum Computing
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.
Terms: Win | Units: 3
Instructors: Bouland, A. (PI)

CS 261: Optimization and Algorithmic Paradigms
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.
Terms: Spr | Units: 3
Instructors: Goel, A. (PI)

CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309)
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.
Terms: Win | Units: 3
Instructors: Valiant, G. (PI) ; Wootters, M. (PI)

CS 270: Modeling Biomedical Systems (BIOMEDIN 210)
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.
Terms: Win | Units: 3
Instructors: Musen, M. (PI)

CS 271: Artificial Intelligence in Healthcare (BIODS 220, BIOMEDIN 220)
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)…
Terms: Aut | Units: 3-4
Instructors: Yeung, S. (PI) ; Burgess, J. (TA) ; Gupte, S. (TA)

CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)
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.
Terms: Spr | Units: 3-5
Instructors: Altman, R. (PI)

CS 273C: Cloud Computing for Biology and Healthcare (BIOMEDIN 222, GENE 222)
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.
Terms: Spr | Units: 3
Instructors: Snyder, M. (PI)

CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
Topics: This is a graduate level 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, 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, chemoinformatics, pharmacogenetics, network biology. Note: For Fall 2021, Dr. Altman will be away on sabbatical and so class will be taught from lecture videos recorded in fall of 2018. The class will be entirely online, with no scheduled meeting times. Lectures will be released in batches to encourage pacing. A team of TAs will manage all class logistics and grading. Firm prerequisite: CS 106B.
Terms: Aut | Units: 3-4
Instructors: Altman, R. (PI) ; Aklilu, J. (TA) ; Carpenter, K. (TA) …

CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 217, GENE 217)
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.
Terms: Win | Units: 3-4
Instructors: Wall, D. (PI)

CS 275A: Symbolic Musical Information (MUSIC 253)
Properties of symbolic data for music applications including advanced notation systems, data durability, mark-up languages, optical music recognition, and data-translation tasks. Hands-on work involves these digital score formats: Guido Music Notation, Humdrum, MuseData, MEI, MusicXML, SCORE, and MIDI internal code.
Terms: Win | Units: 2-4
Instructors: Sapp, C. (PI) ; Selfridge-Field, E. (PI)

CS 275B: Computational Music Analysis (MUSIC 254)
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.
Terms: Spr | Units: 2-4
Instructors: Sapp, C. (PI) ; Selfridge-Field, E. (PI)

CS 278: Social Computing (SOC 174, SOC 274)
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 systems and the foundational ideas that underpin them. Students will engage in the creation of new computationally-mediated social environments. Course available for 3-4 units; students enrolling in the 4-unit option will conduct deeper engagement with the topic via additional readings and discussions.
Terms: Spr | Units: 3-4
Instructors: Wodtke, C. (PI)

CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279)
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.
Terms: Aut | Units: 3
Instructors: Dror, R. (PI) ; Hotta, H. (TA) ; Luu, J. (TA) …

CS 281: Ethics of Artificial Intelligence
Machine learning has become an indispensable tool for creating intelligentnapplications, accelerating scientific discoveries, and making better data-drivenndecisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.
Terms: Spr | Units: 3-4
Instructors: Guestrin, C. (PI)

CS 298: Seminar on Teaching Introductory Computer Science (EDUC 298)
Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Terms: Spr | Units: 1
Instructors: Gregg, C. (PI)

CS 300: Departmental Lecture Series
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.
Terms: Aut | Units: 1
Instructors: Reingold, O. (PI)

CS 320: Value of Data and AI
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.
Terms: Win | Units: 3
Instructors: Eglash, S. (PI) ; Zaharia, M. (PI) ; Zou, J. (PI)

CS 322: Triangulating Intelligence: Melding Neuroscience, Psychology, and AI (PSYCH 225)
This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas. Recommended prerequisites: PSYCH 1, PSYCH 24/ SYMSYS 1/ CS 24, CS 221, CS 231N
Terms: Win | Units: 3
Instructors: Gweon, H. (PI) ; Wu, J. (PI) ; Yamins, D. (PI)

CS 324: Understanding and Developing Large Language Models
The field of natural language processing (NLP) has been transformed by massive pre-trained language models. They form the basis of all state-of-the-art systems across a wide range of tasks and have shown an impressive ability to generate fluent text and perform few-shot learning. At the same time, these models are hard to understand and give rise to new ethical and scalability challenges. In this course, students will learn the fundamentals about the modeling, theory, ethics, and systems aspects of massive language models, as well as gain hands-on experience working with them.
Terms: Win | Units: 3
Instructors: Hashimoto, T. (PI) ; Liang, P. (PI) ; Re, C. (PI)

CS 325B: Data for Sustainable Development (EARTHSYS 162, EARTHSYS 262)
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.
Terms: Aut | Units: 3-5 | Repeatable for credit
Instructors: Burke, M. (PI) ; Ermon, S. (PI) ; Lobell, D. (PI) ; Meng, C. (TA)

CS 326: Topics in Advanced Robotic Manipulation
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.
Terms: Aut | Units: 3-4
Instructors: Bohg, J. (PI) ; Chen, C. (TA)

CS 329D: Machine Learning Under Distributional Shifts
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).
Terms: Aut | Units: 3
Instructors: Hashimoto, T. (PI) ; Gulrajani, I. (TA)

CS 329E: Machine Learning on Embedded Systems (EE 292D)
This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. In this class students will learn about techniques to optimize machine learning models and deploy them on a device such as a Arduino, Raspberry PI, Jetson, or Edge TPUs. The class has a significant project component. Prerequisites: CS 107(required), CS 229 (recommended), CS 230 (recommended).
Terms: Aut | Units: 3
Instructors: Asgar, Z. (PI) ; Katti, S. (PI) ; Zhang, K. (TA)

CS 329P: Practical Machine Learning
Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models. This class will teach both statistics, algorithms and code implementations. Homeworks and the final project emphasize solving real problems. nnPrerequisites: Python programing and machine learning ( CS 229), basic statistics.nnPlease view course website here: https://c.d2l.ai/stanford- cs329p/
Terms: Aut | Units: 3-4
Instructors: Huang, Q. (PI) ; Li, M. (PI) ; Smola, A. (PI) …

CS 329S: Machine Learning Systems Design
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 data management, data engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects. 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.
Terms: Win | Units: 3-4
Instructors: Nguyen, H. (PI)

CS 329T: Trustworthy Machine Learning
This course will provide an introduction to state-of-the-art ML methods designed to make AI more trustworthy. The course focuses on four concepts: explanations, fairness, privacy, and robustness. We first discuss how to explain and interpret ML model outputs and inner workings. Then, we examine how bias and unfairness can arise in ML models and learn strategies to mitigate this problem. Next, we look at differential privacy and membership inference in the context of models leaking sensitive information when they are not supposed to. Finally, we look at adversarial attacks and methods for imparting robustness against adversarial manipulation.nnStudents will gain understanding of a set of methods and tools for deploying transparent, ethically sound, and robust machine learning solutions. Students will complete labs, homework assignments, and discuss weekly readings. Prerequisites: CS229 or similar introductory Python-based ML class; knowledge of deep learning such as CS230, CS231N; familiarity with ML frameworks in Python (scikit-learn, Keras) assumed.
Terms: Spr | Units: 3
Instructors: Mitchell, J. (PI)

CS 330: Deep Multi-task and Meta Learning
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 be leveraged 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 and implement 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.
Terms: Aut | Units: 3
Instructors: Finn, C. (PI) ; Hausman, K. (PI) ; Ali, K. (TA) …

CS 333: Algorithms for Interactive Robotics
AI agents need to collaborate and interact with humans in many different settings such as bots operating on social media and crowdsourcing platforms, AI assistants brokering transactions on electronic marketplaces, autonomous vehicles driving alongside humans, or robots interacting with and assisting humans in homes. Our goal in this class is to learn about and design algorithms that enable robots and AI agents 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 studies algorithms in robotics, machine learning, and control theory, which can improve the state-of-the-art human-AI systems. nnRecommended: Introductory course in AI ( CS 221) and Machine Learning ( CS 229).
Terms: Win | Units: 3-4
Instructors: Sadigh, D. (PI)

CS 335: Fair, Accountable, and Transparent (FAccT) Deep Learning
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.
Terms: Spr | Units: 3
Instructors: Landay, J. (PI)

CS 337: AI-Assisted Care (MED 277)
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.
Terms: Aut | Units: 1-4
Instructors: Adeli, E. (PI) ; Kaushal, A. (PI) ; Li, F. (PI) …

CS 342: Building for Digital Health (MED 253)
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.
Terms: Win | Units: 3
Instructors: Aalami, O. (PI)

CS 343D: Domain-Specific Programming Models and Compilers
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: CS143 or equivalent
Terms: Aut | Units: 3
Instructors: Kjoelstad, F. (PI) ; Donovick, C. (TA)

CS 347: Human-Computer Interaction: Foundations and Frontiers
(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.
Terms: Spr | Units: 3-4 | Repeatable for credit
Instructors: Agrawala, M. (PI)

CS 348B: Computer Graphics: Image Synthesis Techniques
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.
Terms: Spr | Units: 3-4
Instructors: Fatahalian, K. (PI) ; James, D. (PI)

CS 348C: Computer Graphics: Animation and Simulation
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.
Terms: Win | Units: 3
Instructors: James, D. (PI)

CS 348E: Character Animation: Modeling, Simulation, and Control of Human Motion
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
Terms: Spr | Units: 3
Instructors: Liu, K. (PI)

CS 348I: Computer Graphics in the Era of AI
This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and imaging. 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, view synthesis, colorization, style transfer, motion synthesis, differentiable physics simulation, and reinforcement learning. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, apply neural procedural modeling for shape and scene synthesis, exploit data-driven methods for simulating physical phenomena, and implement policy learning algorithms for creating character animation. Recommended Prerequisites: CS148, CS231N
Terms: Aut | Units: 3-4
Instructors: Liu, K. (PI) ; Wu, J. (PI) ; Clarke, S. (TA)

CS 348K: Visual Computing Systems
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).
Terms: Spr | Units: 3-4
Instructors: Fatahalian, K. (PI)

CS 348N: Neural Models for 3D Geometry
Course Description: Generation of high-quality 3D models and scenes by leveraging machine learning tools and approaches. Survey of geometry representations. Public 3D object and scene data sets. Neural architectures for geometry, including deep architectures for point clouds and meshes. Generative models for 3D: autoencoders, GANs, neural implicits, neural ODEs, autoregressive models. Conditional generation based on images or partial geometry. Variation generation. Evaluation metrics for content generation. Use of synthetic data in ML training pipelines. Prerequisites: CS148 and the rudiments of deep learning. Recommended: CS229.
Terms: Win | Units: 3
Instructors: Guibas, L. (PI)

CS 349D: Cloud Computing Technology
The largest change in the computer industry over the past ten years has arguably been the emergence of cloud computing: organizations are increasingly moving their workloads to managed public clouds and using new, global-scale services that were simply not possible in private infrastructure. However, both building and using cloud systems remains a black art with many difficult research challenges. This research seminar will cover the latest advances in cloud computing from both industry and academic work and survey challenges including programming interfaces, cloud native applications, resource management, pricing, availability and reliability, privacy and security. Students will propose and develop an original research project in cloud computing. nnPrerequisites: For graduate students, background in computer systems recommended but not required ( CS 140/240, 144/244, 244B or 245). Undergrads will need instructor’s approval.
Terms: Aut | Units: 3
Instructors: Kozyrakis, C. (PI) ; Kaffes, K. (TA)

CS 349H: Software Techniques for Emergent Hardware Platforms (EE 292Y)
Research seminar on software techniques for emergent computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on thensemester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, exible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms. Prerequisites: CS107 or CS107E (required) and EE180 (recommended).
Terms: Aut | Units: 3
Instructors: Achour, S. (PI) ; Andrikopoulos, S. (TA)

CS 354: Topics in Intractability: Unfulfilled Algorithmic Fantasies
Over the past 45 years, understanding NP-hardness has been an amazingly useful tool for algorithm designers. This course will expose students to additional ways to reason about obstacles for designing efficient algorithms. Topics will include unconditional lower bounds (query- and communication-complexity), total problems, Unique Games, average-case complexity, and fine-grained complexity. Prerequisites: CS 161 or equivalent. CS 254 recommended but not required.
Terms: Win | Units: 3
Instructors: Rubinstein, A. (PI)

CS 355: Advanced Topics in Cryptography
Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. May be repeated for credit. Prerequisite: CS255.
Terms: Spr | Units: 3 | Repeatable for credit
Instructors: Ozdemir, A. (PI)

CS 356: Topics in Computer and Network Security
Research seminar covering foundational work and current topics in computer and network security. Students will read and discuss published research papers as well as complete an original research project in small groups. Open to Ph.D. and masters students as well as advanced undergraduate students. Prerequisites: While the course has no official prerequisites, students need a mature understanding of software systems and networks to be successful. We strongly encourage students to first take CS155: Computer and Network Security.
Terms: Win | Units: 3
Instructors: Durumeric, Z. (PI)

CS 357S: Formal Methods for Computer Systems
The complexity of modern computer systems requires rigorous and systematic verification/validation techniques to evaluate their ability to correctly and securely support application programs. To this end, a growing body of work in both industry and academia leverages formal methods techniques to solve computer systems challenges. This course is a research seminar that will cover foundational work and current topics in the application of formal methods-style techniques (some possible examples include SAT/SMT, model checking, symbolic execution, theorem proving, program synthesis, fuzzing) to reliable and secure computer systems design. The course can be thought of as an applied formal methods course where the application is reliable and secure architecture, microarchitecture, and distributed systems design. Prior formal methods experience is not necessary. Students will read and discuss published research papers and complete an original research project. Open to PhD and masters students as well as advanced undergraduate students. Prerequisites: EE180 Digital Systems Architecture or comparable course, or consent of instructor.
Terms: Aut | Units: 3
Instructors: Trippel, C. (PI) ; Wu, H. (TA)

CS 359D: Hardness of Approximation
Results on and proof techniques for ruling out good approximation algorithms for NP-hard optimization problems. Topics: the PCP theorem; parallel repetition theorem; the unique games conjecture; applications to set cover, clique, max cut, network design, and problems. Prerequisites: 154 and 261, or equivalents.
Terms: Spr | Units: 3
Instructors: Bouland, A. (PI)

CS 360: Simplicity and Complexity in Economic Theory (ECON 284)
Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.
Terms: Spr | Units: 3-5
Instructors: Akbarpour, M. (PI)

CS 361: Engineering Design Optimization (AA 222)
Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.
Terms: Spr | Units: 3-4
Instructors: Kochenderfer, M. (PI)

CS 366: Computational Social Choice (MS&E 336)
An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Aut | Units: 3
Instructors: Goel, A. (PI)

CS 369Z: Dynamic Data Structures for Graphs
With the increase of huge, dynamically changing data sets there is a raising need for dynamic data structures to represent and process them. This course will present the algorithmic techniques that have been developed for dynamic data structures for graphs and for point sets.
Terms: Aut | Units: 3
Instructors: Charikar, M. (PI) ; Henzinger, M. (PI) ; Axelrod, B. (TA)

CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371)
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite: CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Win | Units: 3
Instructors: Dror, R. (PI)

CS 372: Artificial Intelligence for Disease Diagnosis and Information Recommendations
Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.
Terms: Spr | Units: 3

CS 377G: Designing Serious Games
Over the last few years we have seen the rise of “serious games” to promote understanding of complex social and ecological challenges, and to create passion for solving them. This project-based course provides an introduction to game design principals while applying them to games that teach. Run as a hands-on studio class, students will design and prototype games for social change and civic engagement. We will learn the fundamentals of games design via lecture and extensive reading in order to make effective games to explore issues facing society today. The course culminates in an end-of- quarter open house to showcase our games. Prerequisite: CS147 or equivalent. 247G recommended, but not required.
Terms: Win | Units: 3-4
Instructors: Wodtke, C. (PI)

CS 377Q: Designing for Accessibility (ME 214)
Designing for accessibility is a valuable and important skill in the UX community. As businesses are becoming more aware of the needs and scope of people with some form of disability, the benefits of universal design, where designing for accessibility ends up benefiting everyone, are becoming more apparent. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility through individual assignments. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability.
Terms: Spr | Units: 3-4
Instructors: Tang, J. (PI)

CS 377U: Understanding Users
This project-based class focuses on understanding the use of technology in the world. Students will learn generative and evaluative research methods to explore how systems are appropriated into everyday life in a quarter-long project where they design, implement and evaluate a novel mobile application. Quantitative (e.g. A/B testing, instrumentation, analytics, surveys) and qualitative (e.g. diary studies, contextual inquiry, ethnography) methods and their combination will be covered along with practical experience applying these methods in their project. Prerequisites: CS 147, 193A/193P (or equivalent mobile programming experience).
Terms: Spr | Units: 3-4
Instructors: Bentley, F. (PI)

CS 379C: Computational Models of the Neocortex
This class focuses on building agents that achieve human-level performance in specialized technical domains and are adept at collaborating with humans using natural language. We draw upon research in cognitive and systems neuroscience to take advantage of what is known about how humans communicate and solve problems in order to design advanced artificial neural network architectures. For more detail on invited speakers, schedule of talks and project milestones, see here: https://web.stanford.edu/class/cs379c/class_messages_listing/curriculum/
Terms: Spr | Units: 3
Instructors: Dean, T. (PI)

CS 390A: Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 390B: Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 390C: Curricular Practical Training
Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS 390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Achour, S. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) …

CS 390D: Part-time Curricular Practical Training
For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science PhD students engage in research and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. Students on F1 visas should be aware that completing 12 or more months of full-time CPT will make them ineligible for Optional Practical Training (OPT).
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 393: Computer Laboratory
For CS graduate students. A substantial computer program is designed and implemented; written report required. Recommended as a preparation for dissertation research. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut | Units: 1-9 | Repeatable for credit

CS 395: Independent Database Project
For graduate students in Computer Science. Use of database management or file systems for a substantial application or implementation of components of database management system. Written analysis and evaluation required. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut | Units: 1-6 | Repeatable for credit
Instructors: Roughgarden, T. (PI)

CS 398: Computational Education
This course covers cutting-edge education algorithms used to model students, assess learning, and design widely deployable tools for open access education. The goal of the course is for you to be ready to lead your own computation education research project. Topics include knowledge tracing, generative grading, teachable agents, and challenges and opportunities implementing computational education in diverse contexts around the world. The course will consist of group and individual work and encourages creativity. Recommended: CS 142 and/or CS 221. Prerequisites: CS 106B and 109.
Terms: Spr | Units: 4
Instructors: Piech, C. (PI)

CS 399: Independent Project
Letter grade only. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. Letter grade; if not appropriate, enroll in CS399P. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 399P: Independent Project
Graded satisfactory/no credit. This course is for masters students only. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. S/NC only; if not appropriate, enroll in CS399. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 422: Interactive and Embodied Learning (EDUC 234A)
Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction. Prerequisites: CS229, CS231N, CS234 (or equivalent).
Terms: Win | Units: 3
Instructors: Haber, N. (PI) ; Li, F. (PI)

CS 428B: Probabilistic Models of Cognition: Language (LINGUIST 238B, PSYCH 220B)
How can we understand natural language use in computational terms? This course surveys probabilistic models for natural language semantics and pragmatics. It begins with an introduction to the Rational Speech Acts framework for modeling pragmatics as social reasoning. It then explores a variety of phenomena in language meaning and usage. Probabilistic programming will be used as a precise and practical way to express models.
Terms: Aut | Units: 3
Instructors: Goodman, N. (PI) ; Boyce, V. (TA)

CS 432: Computer Vision for Education and Social Science Research (EDUC 463)
Computer vision – the study of how to design artificial systems that can perform high-level tasks related to image or video data (e.g. recognizing and locating objects in images and behaviors in videos) – has seen recent dramatic success. In this course, we seek to give education and social science researchers the know-how needed to apply cutting edge computer vision algorithms in their work as well as an opportunity to workshop applications. Prerequisite: python familiarity and some experience with data.
Terms: Win | Units: 3
Instructors: Haber, N. (PI)

CS 448B: Data Visualization (SYMSYS 195V)
Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of CS147, CS148, or equivalent.
Terms: Aut | Units: 3-4 | Repeatable for credit
Instructors: Agrawala, M. (PI) ; Hadi, S. (TA) ; Kim, D. (TA)

CS 448I: Computational Imaging (EE 367)
Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.
Terms: Win | Units: 3
Instructors: Wetzstein, G. (PI)

CS 476A: Music, Computing, Design: The Art of Design (MUSIC 256A)
This course explores the artful design of software tools, toys, games,ninstruments, and experiences. Topics include programming, audiovisualndesign, strategies for crafting interactive systems, game design, asnwell as aesthetic and social considerations of shaping technology in ournworld today. Course work features several programming assignments withnan emphasis on critical design feedback, reading responses, and an"design your own" final project. Prerequisite: experience in C/C++/Javanor Unity/C#. See https://ccrma.stanford.edu/courses/256a/
Terms: Aut | Units: 3-4
Instructors: Kim, K. (PI) ; Wang, G. (PI)

CS 498C: Introduction to CSCL: Computer-Supported Collaborative Learning (EDUC 315A)
This seminar introduces students to foundational concepts and research on computer-supported collaborative learning (CSCL). It is designed for LSTD doctoral students, LDT masters’ students, other GSE graduate students and advanced undergraduates inquiring about theory, research and design of CSCL. CSCL is defined as a triadic structure of collaboration mediated by a computational artefact (participant-artifact-participant). CSCL encompasses two individuals performing a task together in a short time, small or class-sized groups, and students following the same course, digitally interacting.
Terms: Win | Units: 3
Instructors: Pea, R. (PI) ; Vishwanath, A. (TA)

CS 499: Advanced Reading and Research
Letter grade only. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Letter grade; if not appropriate, enroll in CS499P.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 499P: Advanced Reading and Research
Graded satisfactory/no credit. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. S/NC only; if not appropriate, enroll in CS499.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …

CS 520: Knowledge Graphs
Knowledge graphs have emerged as a compelling abstraction for organizing world’s structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar and will include lectures on knowledge graph topics (e.g., data models, creation, inference, access) and invited lectures from prominent researchers and industry practitioners. The seminar emphasizes synthesis of AI, database systems and HCI in creating integrated intelligent systems centered around knowledge graphs.
Terms: Spr | Units: 1
Instructors: Chaudhri, V. (PI) ; Genesereth, M. (PI)

CS 521: Seminar on AI Safety
In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. We will explore some of the major problems in this area from the viewpoint of industry and academia. We plan to have a weekly seminar speaker to discuss issues such as verification of AI systems, reward misalignment and hacking, secure and attack-resilient AI systems, diagnosis and repair, issues regarding policy and ethics, as well as the implications of AI safety in automotive industry. Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended.
Terms: Spr | Units: 1
Instructors: Corso, A. (PI)

CS 522: Seminar in Artificial Intelligence in Healthcare
Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at shift.stanford.edu/healthai.
Terms: Aut | Units: 1
Instructors: Dror, R. (PI)

CS 523: Research Seminar in Computer Vision + X
With advances in deep learning, computer vision (CV) has been transforming all sorts of domains, including healthcare, human-computer interaction, transportation, art, sustainability, and so much more. In this seminar, we investigate its far-reaching applications, with a different theme chosen as the focus each quarter (e.g. the inaugural quarter was CV + Healthcare; the theme for the quarter will be listed on the class syllabus). Throughout the quarter, we deeply examine these themes in CV + X research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and other domains. Each week, students will read and prepare questions and reflections on an assigned paper authored by that week’s speaker. We highly encourage students who are interested in taking an interactive, deep dive into CV research literature to apply. While there are no hard requirements, we strongly suggest having the background and fluency necessary to read and analyze AI research papers (thus MATH 51 or linear algebra, and at least one of CS 231x, 224x, 221, 229, 230, 234, 238, AI research experience for CV and AI fundamentals may be helpful).
Terms: Spr | Units: 1-2
Instructors: Gong, J. (PI)

CS 528: Machine Learning Systems Seminar
Machine learning is driving exciting changes and progress in computing systems. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? What challenges does industry face when deploying machine learning systems in the real world, and how can new system designs meet those challenges?nIn this weekly talk series, we will invite speakers working at the frontier of machine learning systems, and focus on how machine learning changes the modern programming stack. Topics will include programming models for ML, infrastructure to support ML applications such as ML Platforms, debugging, parallel computing, and hardware for ML.nMay be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Fu, D. (PI) ; Goel, K. (PI) ; Kazhamiaka, F. (PI) …

CS 529: Robotics and Autonomous Systems Seminar (AA 289)
Seminar talks by researchers and industry professionals on topics related to modern robotics and autonomous systems. Broadly, talks will cover robotic design, perception and navigation, planning and control, and learning for complex robotic systems. May be repeated for credit.
Terms: Win, Spr | Units: 1 | Repeatable for credit (up to 99 units total)
Instructors: Bohg, J. (PI) ; Pavone, M. (PI)

CS 547: Human-Computer Interaction Seminar
Weekly speakers on human-computer interaction topics. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit
Instructors: Agrawala, M. (PI) ; Follmer, S. (PI) ; Wodtke, C. (PI) ; Liu, S. (TA)

CS 802: TGR Dissertation
Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) …