CS101 is effectively an alternative to CS105. CS 275A. CS 252. 3 Units. See http://graphics.stanford.edu/courses for offerings and prerequisites. Same as: II. Restricted to Computer Science students. 2 Units. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets. degree in Computer Science is intended as a terminal professional degree and does not lead to the Ph.D. degree. Work in the course consists of reading, class discussion, and practical exercises. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs. CS 348B. 2-4 Units. 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. 3 Units. CS 379. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. CS 398. Primary focus on enabling students to build apps for both iOS and Android using RN. Same as: EDUC 211. Two-quarter project course. CS106B is required; CS107 is strongly recommended. CS 225A. CS 217. 3-4 Units. Prerequisite: CS 106A or equivalent. Prerequisites: linear algebra and programming at the undergraduate level. 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. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. 3-4 Units. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, CS147 or CS247. S/NC only; if not appropriate, enroll in CS399. Prerequisite: CS 106B or equivalent. No prior experience with counterfactual or ¿what if¿ reasoning, nor probability, is required. Departmental Lecture Series. Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Advanced Topics in Networking. 3 Units. Computers, Ethics, and Public Policy. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. Problem-Solving for the CS Technical Interview. To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. 3-4 Units. A laptop computer is recommended for the in-class exercises. 1 Unit. This course will focus on the technical mechatronic skills as well as the human factors and interaction design considerations required for the design of smart products and devices. CS 224N. Prerequisite: excellence in 106A or equivalent, or consent of instructor. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how … Interactive Computer Graphics. 3 Units. Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. Generative models are widely used in many subfields of AI and Machine Learning. 3 Units. The labs will be more specialized, with an emphasis on research-worthy topics and techniques. What is computation? Industrial Lectureships in Computer Science. 3 Units. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. CS 254 recommended but not required. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Build mobile applications using tools and APIs in iOS. 3 Units. Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. May be repeated for credit. Prerequisite: 106A or equivalent. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. CS 170. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. 3-4 Units. 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. For most of you, however, the right place to start is with the CS 106 series. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats. 3 Units. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. 3-4 Units. Ethical theory, and social, political, and legal considerations. This class will culminate in an open-ended final project, which the teaching team will help you on. Stanford CS Education Library This online library collects education CS material from Stanford courses and distributes them for free. Recent advances in computing may place us at the threshold of a unique turning point in human history. Graded satisfactory/no credit. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Prerequisites: Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Recommended: Introductory course in AI, machine learning, and robotics. Enrollment in WIM version of the course is limited to 120 students. Arrangements of curves and surfaces. This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Law for Computer Science Professionals. 3 Units. Project topics will be selected from a diverse array of computer graphics concepts and historical elements. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. 2-4 Units. Concurrent enrollment in CS 107 required. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. Recent papers from the literature will be presented and discussed. Random sampling methods. 3-4 Units. 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. The CS curriculum provides knowledge that is applicable across many fields, including many areas of engineering, science, and medicine. Programming, audiovisual design, as well as software design for musical tools, instruments, toys, and games. In this course, we will discuss several success stories at the intersection of algorithm design and machine learning, focusing on devising appropriate models and mathematical tools to facilitate rigorous analysis. 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. Geometric searching and optimization. The course will introduce ideas from computational genomics, machine learning and natural language processing. Students will be introduced to and work with popular deep learning software frameworks. 1-9 Unit. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. 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. Not a programming course. 1 Unit. 3-4 Units. CS 47. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses. Hands-on game development in C++ using Unreal Engine 4, the game engine that triple-A games like Fortnite, PUBG, and Gears of War are all built on. Topics include network infrastructure: data center fabrics, ultra-low latency trading systems; cloud computing infrastructure: building large-scale risk computation platforms using virtual machines, containers and serverless computing. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. Open to both undergraduate and graduate students. CS 187. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. CS 395. See IntroSems Related to this Major. Design for Artificial Intelligence. An onramp for students interested in breaking new ground in the frontiers of computer science. Same as: EE 192T. Some exposure to programming is required. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. Topics in Computer Graphics. 3-5 Units. Can we predict human behavior? Same as: MUSIC 254. 3-4 Units. Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended. 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. High-level Vision: From Neurons to Deep Neural Networks. Topics include knowledge tracing, generative grading, teachable agents, and challenges and opportunities implementing computational education in diverse contexts around the world. In this hands-on project-based course, students will learn about future opportunities and present realities for autonomous robots that provide physical assistance to humans. CS 250. From Languages to Information. Additional Topics in Teaching Computer Science. Focus is on real-world software development. CS 273B. CS 51. This is a course at the intersection of philosophical logic and artificial intelligence. CS 338. CS 332. 3-4 Units. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. Computational Music Analysis. Student diversity of background will be valued highly. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. The first digit of a CS course number indicates its general level of difficulty: 0-99 service course for non-technical majors. Prerequisite: CS106B, CS106X, or equivalent. 3-5 Units. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Same as: MUSIC 128. 3-4 Units. Register using the section number associated with the instructor. 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. Speakers from the games industry will provide insights and context during a weekly seminar. By the end of this course, you will have tangible insights and methods to regain control over your relationship with technology. 1-15 Unit.    Trademark Notice. 1 Unit. CS 193Q. CS 144. CS 529. The course will be taught through a combination of lecture and project sessions. 3 Units. Convolutional Neural Networks for Visual Recognition. 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?. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material. Classroom meetings will be used to foster student project discussions, and deepen understanding of material. This course will require you to learn a new programming language (Swift) as well as a new-to-iOS development environment, SwiftUI. Directed research under faculty supervision. Prerequisites: CS 224N and 224U. Over the years, many powerful algorithms have been built via tools such as linear programming relaxations, spectral properties of graphs, and others, that all bridge the discrete and continuous worlds. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. CS 343D. prior experience training a deep learning model). 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. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Register using instructor's section number. 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. © Stanford University, Stanford, California 94305. CS101 is not a complete programming course such as CS106A. CS 140. CS 294S. Any other courses that help to develop your maturity as a programmer are also recommended. CS 239. Concurrent enrollment in CS 109 required. 3-5 Units. What problems can be solved with computers? Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. Computer Vision: Foundations and Applications. 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. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. Open to Ph.D. and masters students as well as advanced undergraduate students. 3-5 Units. Prior knowledge of basic cognitive science or neuroscience not required but helpful. Prerequisites: CS 221 or AA 238/CS 238 or CS 234 or CS 229 or similar experience. Prerequisite: CS 106A or equivalent, and an introductory course in biology or biochemistry. 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. Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. Prerequisites: 223A or equivalent. 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. Knowledge graphs have emerged as a compelling abstraction for organizing world'snstructured knowledge over the internet, capturing relationships among key entities ofninterest to enterprises, and a way to integrate information extracted from multiplendata sources. The availability of massive datasets is revolutionizing science and industry. Writing Intensive Research Project in Computer Science. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. CS 203. Introduction to Human-Computer Interaction Design. CS 329. Recent offerings have covered the foundations of static analysis, including decision procedures for important theories (SAT, linear integer constraints, SMT solvers), model checking, abstract interpretation, and constraint-based analysis. The Human Genome Source Code. Same as: INTLPOL 251. a vector). Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. CS 368. Students will learn the model of quantum computation, quantum programming languages, hybrid quantum/classical programming, quantum algorithms, quantum error correction, and applications. Computer Science Computer Security HCI Robotics Data Science Biomedical Informatics ... Stanford School of Earth, Energy and Environmental Sciences Stanford School of Medicine ... A Course in Bayesian Statistics. This course will cover various algorithm design techniques for two intimately connected class of problems: sampling from complex probability distributions and counting combinatorial structures. 1 Unit. CS 194A. Introduction to Cryptography. Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. 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). Same as: BIOE 212, BIOMEDIN 212, GENE 212. 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. But to facilitate planning and confirm interest, please fill out a consent application ( https://forms.gle/hLAQ7JUm2jFTWQzE9) by March 13, 2020. Students will work in groups to present a final project in building an application for the Oculus Go headset. Same as: LINGUIST 285. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Toward this end, students work in interdisciplinary teams on a final project and propose a solution that tackles a significant societal challenge by leveraging technology and frameworks on human thriving. Examines key concepts of Android programming: tool chain, application life-cycle, views, controls, intents, designing mobile UIs, networking, threading, and more. CS 155. Same as: EE 368. 2 Units. Available as a substitute for CS110 that fulfills any requirement satisfied by CS110. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. 3-5 Units. Topics in Programming Language Theory. Person with a partner organization primary literature cover fundamental concepts in a sentence ) into a mathematical form then. The geopolitical balance of power, and natural language processing sits at the level of CS 221 would. Be introduced to and work on case studies from healthcare, autonomous driving, sign language,... Required but helpful researching and developing your own computation education research project into six classes that can admitted. 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