Concurrent enrollment in CS 106A required. This is typically held after all or a substantial portion of the dissertation research has been completed. Prerequisites: Strong background in software systems, especially databases (CS 245) and distributed systems (CS 244B), and/or machine learning (CS 229). Students who meet the eligibility requirements and wish to be considered for the honors program must submit a written application to the CS undergraduate program office by May 1 of the year preceding the honors work. This project-based course will give creative students an opportunity to work together on revolutionary change leveraging blockchain technology. Campus Map Optimization and Algorithmic Paradigms. Importantly, we will respect decisions regarding the adoption of Credit/No Credit and other grading options during this unprecedented period of COVID-19 disruption, whether they are made by institutions or by individual students. This seminar series brings together a diverse set of experts to provide interdisciplinary perspectives on these media regarding their history, technologies, scholarly research, industry, artistic value, and potential future. This mathematical form is then used by subsequent steps (e.g. Computational Biology and Bioinformatics are practiced at different levels in many labs across the Stanford Campus. Teams will also be provided access to significant computing resources on a commercial public cloud. 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. Same as: MED 277. Prerequisites: CS107 and CS161, or permission from the instructor. The student-faculty ratio at Stanford University is 5:1, and the school has 69.3% of its classes with fewer than 20 students. Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond. 2-4 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. Applications range from the design of aircraft to automated vehicles. Register using the section number associated with the instructor. Similar trends are observed for modern displays pushing the boundaries of resolution, contrast, 3D capabilities, and immersive experiences through the co-design of optics, electronics, and computation. Register using the section number associated with the instructor. See Fig. Admission is by consent of instructor and is limited to 20 students. Not regular? Program Analysis and Optimizations. 3 Units. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Applicants to graduate studies in Computer Science must apply either to the MS program or to the Ph.D. program, and not to both. iOS Application Development. One additional course from the lists above or the following: One course selected from the list above or the following: For this track there is a 10 unit minimum for ENGR Fundamentals and a 29 unit minimum for Depth (for track and elective courses). Concurrent enrollment in CS 107 required. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. CS 339N. CS 529. Topics include knowledge tracing, generative grading, teachable agents, and challenges and opportunities implementing computational education in diverse contexts around the world. Additional problem solving practice for the introductory CS course CS 106A. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. The student-faculty ratio at Stanford University is 5:1, and the school has 69.3% of its classes with fewer than 20 students. Complex problems require sophisticated approaches. Course features many small programming exercises, although no prior programming experience is assumed or required. CS 22A. Programming Abstractions. Same as: CME 309. Students are expected to present one paper in a tutorial, debate a paper once from the Pro and once from the Con side. 3-4 Units. No prerequisite for PhD students or students outside of CS and Symbolic Systems. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. The course will be project based with a substantial final project. Forming the proper representation for a task is an essential problem in modern AI. Department requirements are stated below. Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Computational Methods for Biomedical Image Analysis and Interpretation. Each week consists of in-class activities designed by student groups, local tech companies, and nonprofits. Mining Massive Data Sets. Instruction includes lectures and discussion of readings from primary literature. A candidate is required to complete a program of 45 units. Terminal Graduate Registration (TGR). Recent Dissertations. CS 47. Same as: PSYCH 250. CS229) and basic neural network training tools (eg. 3 Units. 3-4 Units. The Office of Graduate Admissions is proud to be part of Student Affairs, which educates students to make meaningful contributions as citizens of a complex world. CS 275. Prerequisites: CS229, CS231N, CS234 (or equivalent). The Ph.D. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research. Restricted to Computer Science students. 4-2 in the UGHB),  PSYCH 30, or AP Chemistry Credit. Surgical robots developed and implemented clinically on varying scales. For a complete list of those and other academic policies relating to the pandemic, see the "COVID-19 and Academic Continuity" section of this bulletin. Prerequisite: linear algebra. Problem-solving Lab for CS109. Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. 3-4 Units. This course will require you to learn a new programming language (Swift) as well as a new-to-iOS development environment, SwiftUI. 3-4 Units. No prior knowledge of genomics is necessary. Handling genomic data is deceptively easy. 3 Units. 3 Units. Phone: (650) 723-2300 Admissions: admissions@cs.stanford.edu. Gates Computer Science Building 353 Jane Stanford Way Stanford, CA 94305. Android Programming Workshop. 3-4 Units. and inform future choices (can we use historical electronic medical records data about decision made and outcomes, to create better protocols to enhance patient health?). Required work will involve reading and presenting research papers, as well as working in small groups at these open problems and presenting progress. 3 Units. Prerequisites: CS107 and CS161. In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. The JMP bridges philosophical study and inquiry with analytical skills and engineering capacity, allowing for the intersection and unique development of academic areas, especially logic. Students taking CME 104 Linear Algebra and Partial Differential Equations for Engineers are also required to take its prerequisite, CME 102 Ordinary Differential Equations for Engineers. 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). CS 359. Students will be encouraged to synthesize information they have learned across different courses in the major. Mapping complicated metrics of interest to simpler metrics (normed spaces, trees, and so on) gives access to a powerful algorithmic toolkit for approximation algorithms, online algorithms as well as for efficient search and indexing of large data sets. Recommended: Fourier analysis or digital signal processing. Specific areas will include: reasoning about belief and action, causality and counterfactuals, legal and normative reasoning, natural language inference, and Turing-complete logical formalisms including (probabilistic) logic programming and lambda calculus. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. Primary focus on enabling students to build apps for both iOS and Android using RN. Final project. Designing Solutions to Global Grand Challenges. 3-5 Units. Principles of Data-Intensive Systems. CS 58N. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Gates Computer Science Building 353 Jane Stanford Way Stanford, CA 94305. 3 Units. Understand the impact of engineering solutions in a global, economic, environmental, and societal context. CS 472. CS 369H. CS 107. 3 Units. Additionally, up to a maximum of 3 units of 1-2 unit seminars offered in the School of Engineering may be counted as electives. Metric Embeddings and Algorithmic Applications. Students who will benefit most from this class have exposure to AI, such as through projects and related coursework (e.g. 3 Units. Hacking the Pandemic. Exploring Computational Journalism. Same as: ECON 284. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Application required; please see cs51.stanford.edu for more information. Introduction to the fundamentals and analysis specifically needed by engineers to make informed and intelligent financial decisions. Thesis advisers must be members of Stanford’s Academic Council. Hands-on software building. No Verilog or FPGA programming experience is required. Enrollment limited and application required for admittance. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Prerequisites: CS147 or equivalent background in design thinking. Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Topics in the Theory of Computation. Spoken Language Processing. Choose one of the following ten CS degree tracks (a track must consist of at least 25 units and 7 classes): Students may propose an individually designed track. Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Computers, Ethics, and Public Policy. CS 294W. Law, Order, & Algorithms. In this course, we will study the rigorous computer science necessary foundations for FAccT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, accountability, and privacy. Function spaces and functional maps. 10% of its graduate students are part-time graduate students. The course will consist of group and individual work and encourages creativity. We will also cover effective coaching, design of projects and exercises, and curating material in order to maximize the effectiveness of a flipped classroom. 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. Issues related to research reproducibility. Significant amount of additional advanced material and substantially more challenging projects. Prerequisites: familiarity with programming in Python and R. All students with declared joint majors are permitted to complete their degree; faculty and departments are committed to providing the necessary advising support. A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Students may no longer declare this program. Prerequisites include the standard mathematics sequence through MATH 51 (or CME 100). Become familiar with prototype-design tools like Sketch and Marvel while also learning important design concepts in a low-stress environment. This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. 3 Units. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. CS 236G. We will cover fundamental concepts and techniques in this area, including influence and noise sensitivity, polynomial approximation, hypercontractivity, probabilistic invariance principles, and Gaussian analysis. In this regard, a logic program is more of a specification than an implementation; and logic programs are often called runnable specifications. How can information be securely communicated? The program prepares students for careers in government, law, the corporate sector, and for graduate study. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. Student teams under faculty supervision work on research and implementation of a large project in AI. Prerequisites: basic probability and fluency in a high-level programming language. Principles of Computer Systems. Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Recognizable? Introduction to the Theory of Computation. This is a graduate-level course. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. 3-4 Units. Prerequisite: CS 107. You will undoubtedly leave Stanford with the technical skills to excel in your first few jobs. Please see https://cs181.stanford.edu for more information. 1 Unit. In general, the M.S. 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 department expects undergraduate majors in the program to be able to demonstrate the following learning outcomes. 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). CS 269O. Handbook for Undergraduate Engineering Programs (UGHB). This course is about the study of boolean functions from a complexity-theoretic perspective, with an emphasis on analytic methods. Most students planning to obtain the Ph.D. degree should apply directly for admission to the Ph.D. program. Which ones cannot? If you have any questions about the course, please email symsys1staff@gmail.com. CS101 is not a complete programming course such as CS106A. 3-4 Units. CS 309A. Topics in Digital Systems. Knowledge of Matlab or Python highly recommended. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Science project presentation and computer science phd thesis structure. Admissions: admissions@cs.stanford.edu. 2 Units. 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. program committee other coherent programs that meet their goals and satisfy the basic requirements. Same as: WIM. The Joint Major not only blends the intellectual traditions of two Stanford departments-it does so in a way that reduces the total unit requirement for each major. Prerequisites: Programming proficiency in at least one of C, C++, Python, Matlab or Mathematica and tools/frameworks such as OpenCV or Matlab's Image Processing toolbox. 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. 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. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, to apply neural procedural modeling for shape and scene synthesis, to implement policy learning algorithms for creating character animation, and to exploit data-driven methods for simulating physical phenomena. In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. Topics include: bias in data and models, privacy and computational profiling, measuring civility and toxicity online, computational propaganda, manipulation and framing, fairness/equity, power, recommendations and filter bubbles, applications to social good, and philosophical foundations of ethical investigation. CS 110L. In this course we creatively apply information technologies to collectively attack Global Grand Challenges (e.g., global warming, rising healthcare costs and declining access, and ensuring quality education for all). Software Design Studio. Same as: INTLPOL 200, (Formerly SYMSYS 100). Public presentation of final application or research results is required. 3-4 Units. Students interested in learning to use the computer should consider CS 1C, Introduction to Computing at Stanford. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Students will benefit from some background in deep learning (CS 230, CS 231N), computer vision (CS 231A), digital image processing (CS 232) or computer graphics (CS248). Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, and Computer Science. Some students, however, may wish to complete the master’s program before deciding whether to pursue the Ph.D. To give such students a greater opportunity to become familiar with research, the department has a program leading to a master’s degree with distinction in research. The class will also include a guest speaker who will give teaching advice and talk about AI. 1 Unit. There is limited enrollment for this class. The goal of this interdisciplinary class is to bridge the gap between technological and societal objectives: How do we design AI to promote human well-being? Decidable? Introduction to Game Design and Development. CS 343D. What is computation? 3-4 Units. It is the university's home for fundamental research, where free, open, and critical inquiry is pursued across disciplines. CS 335. Combination MS/PhD not offered. Applications to the M.S. 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. Same as: BIOE 196, BIOPHYS 196. © Stanford University, Stanford, California 94305. 3-4 Units. Advanced Reading and Research. © 2020-21 Stanford University. sciences, social sciences, humanities) with sufficient mathematical maturity are welcomed to apply. You want to be the brain, too. degree in Computer Science is intended as a terminal professional degree and does not lead to the Ph.D. degree. Gates Computer Science Building 353 Jane Stanford Way Stanford, CA 94305. CS390A, CS390B, and CS390C may each be taken once. We strongly encourage students to first take CS155: Computer and Network Security. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility. Course offered occasionally. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. The Social & Economic Impact of Artificial Intelligence. Software engineering principles of data abstraction and modularity. Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Law students interested in pursuing an M.S. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. 3 Units. CS 338. To enroll in the class, please fill the following application: https://forms.gle/nDnuR3R6N9LozXUdA. 3-5 Units. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. CS 124. Prerequisite: Strongly recommend CS147, ME216A or a d.school class on needfinding. Code examples of each topic will be provided for students interested in a particular topic, but there will be no required coding components. However, after aligning with a permanent adviser, passing six breadth requirements, and taking classes with four different faculty, a student is eligible to file for candidacy prior to the sixth quarter. Independent study projects (CS 191 Senior Project or CS 191W Writing Intensive Senior Project) require faculty sponsorship and must be approved by the adviser, faculty sponsor, and the CS senior project adviser (Patrick Young). 3 Units. After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. CS 241. Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning. School of Engineering . CS 81SI. This course will cover fundamental concepts and principled algorithms in machine learning. CS 131. Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Will they alter the geopolitical balance of power, and change the nature of warfare? CS 366. We will have several teaching lectures, a number of prominent external guest speakers, as well as presentations by the students on recent papers and their projects. Advanced Topics in Sequential Decision Making. The focus of CS247S is Service Design. Prerequisites: knowledge of the C programming language. 1 Unit. Prerequisite: CS106B or equivalent for grad students. CS 361. Same as: EARTHSYS 162, EARTHSYS 262. 3-4 Units. This course is for PhD students only. Code the Change helps computer scientists use their skills for social change. Minimum Combined GPA for all courses in Engineering Fundamentals and Depth is 2.0. Courses offered by the Department of Computer Science are listed under the subject code CS on the Stanford Bulletin's ExploreCourses web site. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. Phone: (650) 723-2300. CS 272. 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. Research experience is not required, but basic theory knowledge and mathematical maturity are expected. Data for Sustainable Development. Students must take this course before being approved to declare Symbolic Systems as a major. In this course, you will learn to teach project based classes that include critique. 3 Units. CS 107A. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). CS 269G. Course website: http://web.stanford.edu/class/cs257/. When you click Apply Now, you will be asked to enter the user name and password you received after creating your account. Students should consult their department or program's student services office for applicability of Overseas Studies courses to a major or minor program. No more than 45 units of approved courses may be counted toward both degrees. This is a project-based graduate course that covers a broad set of algorithms in robotics, machine learning, and control theory for the goal of developing interactive human-robot systems. Random sampling methods. 3 Units. Natural Language Processing with Deep Learning. Pseudorandomness is the widely applicable theory of efficiently generating objects that look random, despite being constructed using little or no randomness. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Students can replace one of these electives with a course found at. (Previously numbered CS 369G.) 3 Units. Gates Computer Science Building 353 Jane Stanford Way Stanford, CA 94305. Exceptions are made for applicants who are already students at Stanford and are applying to the coterminal program. This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. The Departments of Computer Science and Philosophy offer a joint major program (JMP) for undergraduates who wish to gain mastery and develop skills in these two disciplines. And what about a promising young graduate in Computer Science from Stanford University? CS 349G. There will be optional discussion sections on Fridays. Qualified computer science PhD students engage in research and integrate that work into their academic program. By arrangement. 3 Units. This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Learn about important issues including privacy, fairness, and basic linear algebra, discrete probability and fluency in paper! 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