以下是斯坦福CS類課程列表：

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

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

CS 102. Big Data - Tools and Techniques. 3-4 Units.

Aimed at non-CS undergraduate and graduate students who want to learn the basics of big data tools and techniques and apply that knowledge in their areas of study. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of collecting and analyzing large volumes of data. At the same time, it is surprisingly easy to make errors or come to false conclusions from data analysis alone. This course provides a broad and practical introduction to big data: data analysis techniques including databases, data mining, and machine learning; data analysis tools including spreadsheets, relational databases and SQL, Python, and R; data visualization techniques and tools; pitfalls in data collection and analysis. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science, CS106A, or other equivalent programming experience.

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

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

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

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

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

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

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

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. No prior programming experience required. Summer quarter enrollment is limited. Alternative versions of CS106A may be available which cover most of the same material but in different programming languages.
Same as: ENGR 70A

CS 106AJ. Programming Methodology in JavaScript. 3-5 Units.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the JavaScript programming language. Emphasis is on good programming style and the built-in facilities of the JavaScript language. No prior programming experience required. This course covers most of the same material as CS106A Section 1 in Java and CS 106A Section 3 in Python, but this course uses the JavaScript programming language. To enroll in this class, enroll in CS 106A Section 2 for Fall Qtr. May be taken for 3 units by grad students.

CS 106AP. Programming Methodology in Python. 3-5 Units.

Introduction to the engineering of computer applications in Python, emphasizing modern software engineering principles: decomposition, abstraction, testing and good programming style. This course covers most of the same material as the other versions of CS106A, but using the Python programming language which is popular for general engineering and web development. Required readings will all be available for free on the web. Students are encouraged to bring a laptop to lecture to do the live exercises which are integrated with lecture. No prior programming experience required. To enroll in this class, enroll in CS 106A Section 3.

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

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent. Summer quarter enrollment is limited.
Same as: ENGR 70B

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

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

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

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

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

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

CS 106X. Programming Abstractions (Accelerated). 3-5 Units.

Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Significant amount of additional advanced material and substantially more challenging projects. Some projects may relate to CS department research. Prerequisite: excellence in 106A or equivalent, or consent of instructor.
Same as: ENGR 70X

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

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

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

Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Prerequisite: 106B or X, and consent of instructor. There is a $75 required course fee.

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

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

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

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

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

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

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

In this hands-on, experiential course, students will design and develop virtual reality applications. You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. The class will teach the design best-practices and the creation pipeline for VR applications, and will include tangents that explore sister fields such as augmented reality and 360 video. Students will work in groups to present a final project in building an application for the Oculus Go headset. Enrollment is limited and by rolling application only. Prerequisite: CS 106A or equivalent.

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

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, machine learning classifiers, inverted indices, collaborative filtering, neural embeddings, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, spell checking, recommender systems, chatbots. Prerequisites: CS103, CS107, CS109.
Same as: LINGUIST 180, LINGUIST 280

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

Robots that can navigate space and perform duties, search engines that can index billions of images and videos, algorithms that can diagnose medical images for diseases, or smart cars that 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. Course will introduce a number of fundamental concepts in computer vision and expose students to a number of real-world applications, plus guide students through a series of projects such that they will get to implement cutting-edge computer vision algorithms. Prerequisites: Students should be familiar with Python (i.e. have programmed in Python before) and Linux; plus Calculus & Linear Algebra.

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

Operating systems design and implementation. Basic structure; synchronization and communication mechanisms; implementation of processes, process management, scheduling, and protection; memory organization and management, including virtual memory; I/O device management, secondary storage, and file systems. Prerequisite: CS 110.

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

This is an experimental course offering. Students will implement a simple, clean operating system (virtual memory, processes, file system) on a rasberry pi computer and use the result to run a variety of devices. Enrollment is limited, and students should expect the course to have rough edges since it is the first offering.

CS 141. Introduction to Computer Sound. 3 Units.

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

CS 142. Web Applications. 3 Units.

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment. Prerequisites: CS 107 and CS 108.

CS 143. Compilers. 3-4 Units.

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

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

Principles and practice. Structure and components of computer networks, packet switching, layered architectures. Applications: web/http, voice-over-IP, p2p file sharing and socket programming. Reliable transport: TCP/IP, reliable transfer, flow control, and congestion control. The network layer: names and addresses, routing. Local area networks: ethernet and switches. Wireless networks and network security. Prerequisite: CS 110.

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

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

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

This project-based course provides an introduction to game design covering topics like 2D/3D Art, Audio, User Interfaces, Production, Narrative Design, Marketing, and Publishing. Speakers from the profession will provide relevant context during a weekly seminar. Weekly assignments include in-depth materials and require students to independently create small video games. Classroom meetings will be used to foster student project discussions, and deepen understanding of material. The course culminates with students forming project teams to create a final video game. Assignments will be completed within the Unity game development engine; prior Unity experience is not required. Given class size limitations, an online survey will be distributed before class starts and students will be selected so to achieve a diverse class composition. Prerequisite: CS 106A or equivalent programming experience.

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

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

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

Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Focuses on using OpenGL to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Course will cover fundamental understanding of light and color, as well as how it impacts computer displays and printers. Class will discuss more thoroughly how light interacts with the environment, constructing engineering models such as the BRDF, plus various simplifications into more basic lighting and shading models. Also covers ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Prerequisites: CS 107, MATH 51.

CS 149. Parallel Computing. 3-4 Units.

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

CS 151. Logic Programming. 3 Units.

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

CS 154. Introduction to Automata and Complexity Theory. 3-4 Units.

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

CS 155. Computer and Network Security. 3 Units.

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

CS 157. Computational Logic. 3 Units.

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

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

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

CS 166. Data Structures. 3-4 Units.

Techniques in the design, analysis, and implementation of data structures. Isometries between data structures (including red/black trees and 2-3-4 trees), amortized analysis (including Fibonacci heaps and splay trees), and randomization (including count-min sketches and dynamic perfect hash tables). Data structures for integers and strings (including van Emde Boas trees and suffix trees). Possible additional topics include functional data structures, concurrent data structures, and spatial data structures. Prerequisites: CS107 and CS161.

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

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

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

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

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

Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Prerequisite: 106B or X. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class.
Same as: COMM 181, ETHICSOC 181X, PHIL 75C, POLISCI 181, PUBLPOL 181

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

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

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

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

CS 190. Software Design Studio. 3 Units.

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

CS 191. Senior Project. 1-6 Unit.

Restricted to Computer Science and Computer Systems Engineering 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. Required public presentation of final application or research results. Prerequisite: Completion of at least 135 units.

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

Restricted to Computer Science and Computer Systems Engineering students. Writing-intensive version of CS191. Register using the section number of an Academic Council member. Prerequisite: Completion of at least 135 units.
Same as: WIM

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

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value.

CS 193A. Android Programming. 3 Units.

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

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

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

CS 193P. iOS Application Development. 3 Units.

Tools and APIs required to build applications for the iPhone and iPad platforms using the iOS SDK. User interface design for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Swift programming language. Other topics include: object-oriented database API, animation, multi-threading, networking and performance considerations. Prerequisites: C language and object-oriented programming experience exceeding 106B or X level. Previous completion of any one of the following is required: CS 107<https://explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&q=CS107>, 108 (preferred) or 110. Recommended: UNIX, graphics, databases.

CS 194. Software Project. 3 Units.

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

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

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

CS 194W. Software Project. 3 Units.

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

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

Directed research under faculty supervision. Students are required to submit a written report and give a public presentation on their work.

CS 196. Computer Consulting. 2 Units.

Focus is on Macintosh and Windows operating system maintenance and troubleshooting through hardware and software foundation and concepts. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. Not a programming course. Prerequisite: 1C or equivalent.
Same as: VPTL 196

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

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

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

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

CS 199. Independent Work. 1-6 Unit.

Special study under faculty direction, usually leading to a written report. Letter grade; if not appropriate, enroll in 199P.

CS 199P. Independent Work. 1-6 Unit.

(Staff).

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

For those with limited experience with computers or who want to learn more about Stanford's computing environment. Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Final project. Not a programming course.
Same as: VPTL 1

CS 1U. Practical Unix. 1 Unit.

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

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

An overview of intellectual property law as it relates to computer science and other disciplines, including discussions of patents, trademarks, copyrights, trade secrets, computer fraud litigation and interesting historical tidbits. Emphasis on topics of current interest such as software and business method patents, copyright issues concerning software, music, art and artificial intelligence, and current disputes of note including the recently-settled Waymo v. Uber lawsuit and the ongoing Oracle v. Google, Apple v. Samsung and hiQ v. LinkedIn sagas. Guest lectures typically have covered open source and the free software movement, practical issues for business founders (including corporate formation issues and non-disclosure, non-compete, work-made-for-hire and license agreements), and other pertinent topics. Classes are presented in an open discussion format broadly directed to students with both technical and non-technical backgrounds.

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

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

CS 204. Legal Informatics. 2-3 Units.

Legal informatics based on representation of regulations in computable form. Encoding regulations facilitate creation of legal information systems with significant practical value. Convergence of technological trends, growth of the Internet, advent of semantic web technology, and progress in computational logic make computational law prospects better. Topics: current state of computational law, prospects and problems, philosophical and legal implications. This course is *Cross* listed with LAW 4019. Prerequisite: basic concepts of programming.

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

A survey of numerical approaches to the continuous mathematics used in computer vision and robotics with 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. (Replaces CS205A, and satisfies all similar requirements.) Prerequisites: MATH 51; MATH 104 or 113 or equivalent or comfortable with the associated material.

CS 206. Exploring Computational Journalism. 3 Units.

This project-based course will explore the field of computational journalism, including the use of Data Science, Info Visualization, AI, and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech, and build trust. Admission by application; please email R.B. Brenner at [email protected] to request application.
Same as: COMM 281

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

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

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

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

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

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

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

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

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

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

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

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing, Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience).

CS 223A. Introduction to Robotics. 3 Units.

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

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

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124 or CS121/221.
Same as: LINGUIST 284

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

Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites: CS 124, 221, 224N, or 229.
Same as: LINGUIST 285

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

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221; and logical/semantics such as LINGUIST 130A or B, CS 157, or PHIL150.
Same as: LINGUIST 188, LINGUIST 288

CS 224W. Analysis of Networks. 3-4 Units.

Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with emergence of online social networks and large-scale data availability in biological sciences, this course focuses on the analysis of massive networks which provide many computational, algorithmic, and modeling challenges. This course develops computational tools that reveal how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. nTopics include: how information spreads through society; robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; friend prediction in online social networks; identification of functional modules in biological networks; disease outbreak detection.

CS 225A. Experimental Robotics. 3 Units.

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

CS 227B. General Game Playing. 3 Units.

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

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

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

CS 229. Machine Learning. 3-4 Units.

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Same as: STATS 229

CS 229A. Applied Machine Learning. 3-4 Units.

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

CS 229T. Statistical Learning Theory. 3 Units.

How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. We will present various learning algorithms and prove theoretical guarantees about them. Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (STATS 315A or CS 229).
Same as: STATS 231

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

(Formerly IPS 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 respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Same as: INTLPOL 200

CS 230. Deep Learning. 3-4 Units.

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

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

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

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

Computer Vision has become ubiquitous in our society, with applications innsearch, image understanding, apps, mapping, medicine, drones, andnself-driving cars. Core to many of these applications are the tasks of image classification, localization and detection. This course is a deep dive into details of neural network architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week 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. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning t