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. Fall quarter 2020 will focus on the algorithms that power our modern world -- search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and others. Limited enrollment, permission of instructor required. In either case, approval may consist of a list applicable to all joint-degree students or may be tailored to each individual student program. 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. Doctor of Philosophy [Ph.D.] in Computer Science is a 5-year program. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Phone: (650) 723-2300 Admissions: admissions@cs.stanford.edu. Counting and Sampling. CS 275B. CS 100B. Offices are located in Suites 127 (1st Floor) and 040 (Basement) Phone: 650-723-4284 Consent Application for Non-Law Students: We will try to accommodate all students interested in the course. 3 Units. Computer Graphics: Image Synthesis Techniques. Prerequisites: CS107, MATH51. 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. The 2020-21 offering will be on Non-Euclidean Methods in Machine Learning. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Get real-world experience researching and developing your own social impact project! Enroll in either CS 182 or CS 182W, not both. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf. Writing-intensive version of CS182. Most important computer applications have to reliably manage and manipulate datasets. Detailed reading of 5 selected Ph.D. dissertations within a field of computer science. Advanced Topics in Operating Systems. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. Declaration or Change of Undergraduate Major, Minor, Honors, or Degree Program, admissions requirements and the application deadline, list of courses that satisfy the breadth requirements, Undergraduate Major Unit Requirements and WIMs, Involuntary Leave of Absence and Return Policy, Main Quadrangle • Memorial Court • Oval • White Plaza, Sexual Harassment and Consensual Sexual or Romantic Relationships, Student Non-​Academic Grievance Procedure, Title IX of the Education Amendments of 1972, Visitor Policy • University Statement on Privacy, School of Earth, Energy and Environmental Sciences, Emmett Interdisciplinary Program in Environment and Resources (E-​IPER), Institute for Computational and Mathematical Engineering, Comparative Studies in Race and Ethnicity (CSRE), Division of Literatures, Cultures, and Languages, Russian, East European and Eurasian Studies, Stem Cell Biology and Regenerative Medicine, Mission of the Undergraduate Program in Computer Science, Computer Science Course Catalog Numbering System, Overseas Studies Courses in Computer Science, Joint Major Program: Computer Science and a Humanities Major, Computer Science Major Requirements in the Joint Major Program, Depth Electives for CS Tracks for students completing a Joint Major, Requirement 2: Significant Software Implementation—, Master of Science with Distinction in Research, Teaching and Research Assistantships in Computer Science, Other service courses, basic undergraduate, Advanced undergraduate/beginning graduate, Computational Biology and Interdisciplinary Topics, Introduction to Probability for Computer Scientists, Mechanics, Concepts, Calculations, and Context, Ethics, Public Policy, and Technological Change, Software Project Experience with Corporate Partners, Writing Intensive Research Project in Computer Science, Research Project in Software Systems and Security, Artificial Intelligence: Principles and Techniques, Probabilistic Graphical Models: Principles and Techniques, Natural Language Processing with Deep Learning, Computer Vision: Foundations and Applications, Computer Vision: From 3D Reconstruction to Recognition, Convolutional Neural Networks for Visual Recognition, Continuous Mathematical Methods with an Emphasis on Machine Learning, Computational Methods for Biomedical Image Analysis and Interpretation, Computational Biology: Structure and Organization of Biomolecules and Cells, Interdisciplinary Topics (Offered occasionally), Topics in Artificial Intelligence (with advisor approval), Introduction to Control Design Techniques, Introduction to Stochastic Control with Applications, Dynamic Programming and Stochastic Control, Computation and Cognition: The Probabilistic Approach, Introduction to Statistical Signal Processing, Decision Analysis I: Foundations of Decision Analysis, Decision Analysis II: Professional Decision Analysis, Influence Diagrams and Probabilistics Networks, Representations and Algorithms for Computational Molecular Biology, Introduction to Human-Computer Interaction Design, Introduction to Computer Graphics and Imaging, Operating Systems and Systems Programming, Operating systems design and implementation, Introduction to Game Design and Development, Introduction to the Theory of Computation, Supervised Undergraduate Research (4 units max), Hardware Accelerators for Machine Learning, Cryptocurrencies and blockchain technologies, Randomized Algorithms and Probabilistic Analysis, Incentives in Computer Science (Not Given This Year), Introduction to Biomedical Informatics Research Methodology, Deep Learning in Genomics and Biomedicine, (Robot Perception and Decision Making: not offered this year), Algorithmic Perspective on Machine Learning, Advance Molecular Biology: Epigenetics and Proteostasis, Introduction to Imaging and Image-based Human Anatomy, Operating Systems and Systems Programming (if not counted above), Operating Systems and Systems Programming (, Linear Algebra and Partial Differential Equations for Engineers (Note: students taking, Numerical Solution of Partial Differential Equations, Human-Computer Interaction: Foundations and Frontiers, (Any suffix beyond the course used above), Introduction to the Design of Smart Products, Topics in Computer Networks (3 or more units, any suffix), Topics in Programming Systems (with permission of undergraduate advisor), Performance Engineering of Computer Systems & Networks, (With permission of undergraduate advisor. CS 106E. Undergrads will need instructor's approval. 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). An in-depth treatment of algorithmic and game-theoretic issues in social choice. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103. 3-4 Units. The class involves significant system software implementation and uses an iterative approach consisting of implementation, review, and revision. The department's own recruiting network. Select at least three of the following: C. A total of at least 21 units from categories (A), (B), and the following: B. CS 246. CS 109A. Wellness in Tech: Designing an Intentional Lifestyle in a Tech-Driven World. Register using the section number associated with the instructor. 2 Units. How to Make VR: Introduction to Virtual Reality Design and Development. PhD Electrical Engineering & Computer Science, Massachusetts Institute of Technology. CS106A, CS106B, Recommended: CS193P/A, CS142, CS47, CS110. This is typically held after all or a substantial portion of the dissertation research has been completed. What is HAI? Students will create utility apps or games as a response to the challenges presented. CS 271. Concurrent enrollment in CS 161 required. students may elect to begin their course of study in either the Law School or the Computer Science Department. 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. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. Project proposal form is required before the beginning of the quarter of enrollment: https://cs.stanford.edu/degrees/undergrad/Senior%20Project%20Proposal.pdf. (Previously numbered CS 229A.) 3 Units. 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. CS 131. 3 Units. Enrollment in WIM version of the course is limited to 120 students. A playback show brings about a powerful listening and sharing experience. Two additional courses from the list above or the following: Track Electives: at least three additional courses selected from the list above, the general CS electives list, or the courses listed below. Stanford Advanced Computer Security Certificate As more threats and breaches are discovered, the demand for cybersecurity professionals increases daily. Prerequisite: one of CS147, CS148, or equivalent. We will explore theories and design frameworks from constructivist and constructionist learning perspectives, as well as the lenses of critical pedagogy, Universal Design for Learning (UDL), and interaction design for children. 3 Units. Advanced Multi-Core Systems. CS 213. We will introduce the relevant background for the biology and epidemiology of the COVID-19 virus. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. 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. CS 448P. iOS Application Development. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. CS 373. 3 Units. CS 197. Web or mobile programming experience (e.g., CS 142), or experience with qualitative user studies may be helpful. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. Students must be enrolled full-time in the Law School for the first year of law studies. Prerequisites: CS 145, 161. Topic changes each quarter. Register using the section number associated with the instructor. Students are required to submit a written report and give a public presentation on their work. Restricted to Computer Science and Computer Systems Engineering undergraduates. Open to Ph.D. and masters students as well as advanced undergraduate students. Hacking the Pandemic. Where will we see the next 1000x increases in scale and data volume, and how should data-intensive systems accordingly evolve? Prerequisite: CS 106B or equivalent. CS 254. 3 Units. CS 44N. Two of the depth electives are waived. The fundamentals and state-of-the-art in web security. CS 194H. Prerequisites: linear algebra (MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning (CS 229, STATS 229, or STATS 315A). Prerequisites: At least one of CS107 or CS145. 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. The dissertation adviser is often the student's program adviser. Playback combines elements of theater, community work and storytelling. Our goal in this class is to learn about and design algorithms that enable robots to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on. By the end of the first academic year, you should align with a permanent adviser. There will be optional discussion sections on Fridays. 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. 3-4 Units. Cloud Computing for Biology and Healthcare. The computer science department continues to lead the world in computer science research and education. Class will consist of video tutorials and weekly hands-on lab sections. Prerequisite: CS106B. The Stanford Advanced Computer Security program will prepare you to thrive in this industry with online, self-paced courses taught by world-class Stanford instructors and faculty. 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. Computational Biology in Four Dimensions. Bridging Policy and Tech Through Design. What is it like for graduate students? Over the past 45 years, understanding NP-hardness has been an amazingly useful tool for algorithm designers. Prerequisites: EE180 Digital Systems Architecture or comparable course, or consent of instructor. Biomedical Sciences Stanford MSTP students who are interested in PhD programs within the biomedical sciences, including those within the School of Medicine, the School of Humanities and Sciences (Departments of Biology, Chemistry, and Physics), and the School of Engineering (Bioengineering, Computer Science, Electrical Engineering, and more). 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? 3 Units. Technology for Financial Systems. 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. The Practicum provides the design foundation for EDUC 211 / CS 402 L, a hands-on lab focused on introductory prototyping and the fabrication of incipient interactive, educational technologies. They are intended for students in nontechnical disciplines who expect to make some use of computers, but who do not expect to go on to more advanced courses. This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. What are the theoretical limits of computing power? Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. CS 140. 3 Units. Artificial Intelligence in Healthcare. Data structures: binary search trees, heaps, hash tables. 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). CS 193C. The PhD program in epidemiology and clinical research will provide methodologic and interdisciplinary training that will equip students to carry out cutting-edge epidemiologic research. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Building for Digital Health. After the term begins, students electing the final paper option can transfer from section 1 to section 2, which meets the R requirement, with consent of the instructor. Students build on the teaching skills developed in CS198. 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. Prerequisite: 106B or X or equivalent programming experience. map, filter, zip), and Python libraries. Not regular? Same as: LINGUIST 285. 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. This course is designed as a deep dive into the design, analysis, implementation, and theory of data structures. Prerequisite: CS255. It also provides an overview of different robot system architectures. 2 Units. For students whose principal interest is an exposure to the fundamental ideas behind computer science and programming, CS 101 or CS 105 are the most appropriate courses. Students should consult their department or program's student services office for applicability of Overseas Studies courses to a major or minor program. CS 1C. For M.S. Many fundamental principles, key technologies and important applications lie at the intersection between the two disciplines.The intersection of EE and CS is addressed by many courses, including those listed below. This project class investigates and models COVID-19 using tools from data science and machine learning. Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Topics in Computer Vision. 3 Units. CS 347. An introductory, project-based exploration of systems and processes for making things using computer-aided design and manufacturing, and an introduction to machines and machine tools. Topics in Computer Graphics: Computational Video Manipulation. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Prerequisites: Background in human-centered design (e.g., CS 147, CS 247, ME 115A, or a d.school class) is required. Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a roundtable discussion with the class. Course projects include writing security exploits, defending insecure web apps, and implementing emerging web standards. Students from elsewhere in the University, from engineering to business to the social sciences and beyond, will emerge with an enhanced capacity to critically assess the legal and policy implications of new digital technologies and the ways society can work to ensure those technologies serve the public good. to design educational toolkits, educational toys, science kits, and tangible user interfaces. CS230 and/or CS231n). Department requirements are stated below. 3 Units. Embedded Systems Workshop. This course is ideal for anyone with little to no visual design experience who would like to build their skill set in UI/UX for app or web design. The student must pass a University oral examination in the form of a defense of the dissertation. Principles of Data-Intensive Systems. A Linux or Mac laptop that you are comfortable coding on. Supplemental lab to CS110. Written assignments and programming projects. Topics in Intractability: Unfulfilled Algorithmic Fantasies. 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. 3-4 Units. Introduction to statistical and computational methods for genomics. Prerequisites: 103 or 103B, and 107. Prerequisites: CS107 and CS161, or permission from the instructor. 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. The Mathematics, Science, and Engineering Fundamentals requirements are non-standard for this track. 3 Units. Topics in Computer Graphics: Agile Hardware Design. Students must also take at least three units of coursework from four different faculty members. Degrees for a Program. Network names, addresses and ethernet switching. Prerequisites: CS250 / EE387 or EE388; or linear algebra and permission of the instructor. Prerequisites: A background in logic, at least at the level of PHIL 151, will be expected. 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. 3-5 Units. Working on a real-world challenge related to social health and civic fabric (e.g. The waived depth electives are listed below for each CS track. 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. See Fig. The program trains students in the tools of modern epidemiology, with heavy emphases on statistics, computer science, genetics, genomics, and bioinformatics. Prerequisites: MATH 51; Math104 or MATH113 or equivalent or comfort with the associated material. 3-5 Units. A total of at least 21 units from category (A) and at least 3 of the following: C. A total of at least 21 units from categories (A), (B) and the following: A. Generative models are widely used in many subfields of AI and Machine Learning. A computational primer to "hacking" the most amazing operating system "disk" on the planet: your genome. CS 106AX. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the syntax, structure, and semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required. Same as: BIOMEDIN 210. ), Topics in Intractability: Unfulfilled Algorithmic Fantasies (Not given this year), Advanced Topics in Cryptography (Not given this year), Advanced Topics in Formal Methods (Not given this year), Topics in the Theory of Computation (with permission of undergraduate advisor), Topics in Analysis of Algorithms (with permission of undergraduate advisor), Introduction to Probability and Statistics for Engineers, Advanced Topics in Sequential Decision Making, Research Project in Artificial Intelligence, Automated Reasoning: Theory and Applications, Advanced Robotic Manipulation (Not given this year), Representation Learning in Computer Vision, Advanced Survey of Reinforcement Learning, Metric Embeddings and Algorithmic Applications, Large-Scale Neural Network Modeling for Neuroscience, Inference, Estimation, and Information Processing, Analysis and Control of Nonlinear Systems, Fundamentals of Data Science: Prediction, Inference, Causality, Decision Analysis III: Frontiers of Decision Analysis, Research Project in Software Systems and Security (Not given this year), (HCI Design Studio (any suffix) in addition to the course taken to satisfy category (A)), Computer Graphics: Geometric Modeling & Processing, Computer Graphics: Image Synthesis Techniques, Computer Graphics: Animation and Simulation, Character Animation: Modeling, Simulation, and Control of Human Motion, Topics in Computer Systems (Offered occasionally), Domain-Specific Programming Models and Compilers, Topics in Geometric Algorithms: Non-Euclidean Methods in Machine Learning. 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