Using real world topics, we will study the technical, legal, policy, and business aspects of an incident or issue and its potential solutions. Teams will spend the rest of the quarter applying user-centered design methods to rapidly iterate through design, prototyping, and testing of their solutions. Consult the department web site for details. The course will be project based with a substantial final project. 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. Linear and non-linear dimensionality reduction techniques. CS 52. Ph.D. Minor in Management Science and Engineering. Same as: MUSIC 256A. 3 Units. Prerequisite: CS 106A or equivalent. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. 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 project class investigates and models COVID-19 using tools from data science and machine learning. For the last few weeks, students will work with course staff to develop their own significant Python project. 1 Unit. The student enrolls in CS191 or 191W (3 units) during the senior year. After learning the essential programming techniques and the mathematical foundations of computer science, students take courses in areas such as programming techniques, automata and complexity theory, systems programming, computer architecture, analysis of algorithms, artificial intelligence, and applications. Recommended: Fourier analysis or digital signal processing. Is it ever reasonable to make a decision randomly? Recommended: matrix algebra. We will look at what makes a good or bad user interface, effective design techniques, and how to employ these techniques using Sketch and Marvel to make realistic prototypes. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. Same as: STATS 229. In this course, we look at the theory of Computational Law, we review relevant technology and applications, we discuss the prospects and problems of Computational Law, and we examine its philosophical and legal implications. Prerequisites: 103 or 103B, and 107. CS 294A. 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. Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Machine Learning Theory. 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. Complex problems require sophisticated approaches. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Programming Methodology. The Social & Economic Impact of Artificial Intelligence. Classic and new papers. CS 348A. In playback, as in research, we are always moving together, from the known, to the unknown, and back. Designed as an accessible survey, the course will equip students with two powerful bases of knowledge: (i) a working technical grasp of key digital technologies (e.g., AI and machine learning, internet structure, encryption, blockchain); and (ii) basic fluency in the key legal frameworks implicated by each (e.g., privacy, cybersecurity, anti-discrimination, free speech, torts, procedural fairness). Welcome to the History Department at Stanford University. 1 Unit. 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. Possible additional topics: network flow, string searching. Artificial Intelligence for Disease Diagnosis and Information Recommendations. Software Design Studio. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. CS 205L. Prerequisites: MATH 51; Math104 or MATH113 or equivalent or comfort with the associated material. CS 345S. Phone: (650) 723-2300 Admissions: admissions@cs.stanford.edu. 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. There are NO courses specifically required by the CS Ph.D. program except for the 1 unit, Each student, to remain in the Ph.D. program, must satisfy the breadth requirement covering introductory-level graduate material in major areas of computer science. Recommended Prerequisites: CS248, CS231N, CS229, CS205A. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Exploring Computational Journalism. This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. CS 246. Weekly speakers on human-computer interaction topics. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. This course is about the fundamentals and contemporary usage of the Python programming language. CS 254. Decision Making under Uncertainty. General CS Electives: CS 108, CS 124, CS 131, CS 140 (or CS 140E), CS 142, CS 143, CS 144, CS 145, CS 146, CS 147, CS 148, CS 149, CS 154, CS 155, CS 157(or PHIL 151), CS 163, CS 166, CS 168, CS 190, CS 195 (4 units max), CS 197, CS 205L, CS 210A, CS 217, CS 221, CS 223A, CS 224N, CS 224S, CS 224U, CS 224W, CS 225A, CS 227B, CS 228, CS 229, CS 229M, CS 230, CS 231A, CS 231N, CS 232, CS 233, CS 234CS 234CS 234CS 234CS 234CS 234CS 234CS 234CS 234, CS 235, CS 237A, CS 237B, CS 238, CS 240, CS 240LX, CS 242, CS 243, CS 244, CS 244B, CS 245, CS 246, CS 247(any suffix), CS 248, CS 251, CS 252, CS 254, CS 254B, CS 255, CS 261, CS 263, CS 265, CS 269I, CS 269Q, CS 270, CS 271, CS 272, CS 273A, CS 273B, CS 274, CS 276, CS 278, CS 279, CS 330, CS 336, CS 348 (any suffix), CS 351, CS 352, CS 369L, CS 398, CME 108; EE 180, EE 282. Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. Please note that only current Stanford students are eligible to apply for Ph.D. Minor in Computer Science. Introduction to the fundamental concepts of computer systems. Logic and Artificial Intelligence. Independent Project. Science project presentation and computer science phd thesis structure. May be repeated for credit. Final project. 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). Analysis of Boolean Functions. The BOSP course search site displays courses, locations, and quarters relevant to specific majors. 1 Unit. Directed research under faculty supervision. No required prerequisites. See http://graphics.stanford.edu/courses for offererings and prerequisites. Main class components are workshops, community discussions, guest speakers and mentorship. 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. Courses must be taken for a letter grade and passed with a grade of 'B' or better. Where will we see the next 1000x increases in scale and data volume, and how should data-intensive systems accordingly evolve? Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. It also provides an overview of different robot system architectures. 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. 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. CS390A, CS390B, and CS390C may each be taken once. Problem-solving Lab for CS107. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. Student teams under faculty supervision work on research and implementation of a large project in AI. Seminar covering issues in natural language processing related to ethical and social issues and the overall impact of these algorithms on people and society. Exceptions are made for applicants who are already students at Stanford and are applying to the coterminal program. 3-4 Units. Recommended: CS 106B, CS 42 or 142. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: CS 106A or equivalent, CME 100 or equivalent (for linear algebra), and CME 106 or equivalent (for probability theory). Systems, and Theoretical Computer Science; M.S. Randomized Algorithms and Probabilistic Analysis. Prerequisites: CS106A or equivalent. 3 Units. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). CS 261. Features weekly lectures and a series of small programming projects. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. Information Retrieval and Web Search. 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. After the introductory sequence, Computer Science majors and those who need a significant background in computer science for related majors in engineering should take CS 103, CS 107 and CS 110. Prerequisites: CS 109 and 110. Proposals should include a minimum of 25 units and seven courses, at least four of which must be CS courses numbered 100 or above. A proposal, early in March is required. 1 Unit. 3-4 Units. Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats. 3-4 Units. Representation Learning in Computer Vision. 3 Units. Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Priority given to first-year Computer Science Ph.D. students. Prerequisites: CS106B. Stanford University requires the Test of English as a Foreign Language (TOEFL) from all applicants whose native language is not English. Each module will be explored via a mix of technical and legal instruction, case study discussions, in-class practical exercises, and guest speakers from industry, government, academe, and civil society. Engineering Design Optimization. 3 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. This timely project-based course provides a venue for students to apply their skills in computing and other areas to help people cope with the Coronavirus Disease 2019 (CoViD-19) pandemic. Prerequisites: calculus and linear algebra. CS 146. Recommended: basic Unix. Same as: PSYCH 249. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. MATH 19, MATH 20, and MATH 21, or AP Calculus Credit may be used as long as at least 26 MATH units are taken. Learn basic, foundational techniques for developing Android mobile applications and apply those toward building a single or multi page, networked Android application. At the end, students should expect to have learned a lot more about logic, and also to have a sense for how logic has been and can be used in AI applications. At its best, AI can help humans mitigate climate change, diagnose and treat diseases more effectively, enhance learning, and improve access to capital throughout the world. Computer Vision and Image Analysis of Art. Combination MS/PhD not offered. CS 107A. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. 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). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. You will have discussions criticizing papers and assigning grades to them. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. In this course, closely cotaught by a Stanford professor and a leading Silicon Valley venture capitalist, we will examine the current state of the art capabilities of existing artificial intelligence systems, as well as economic challenges and opportunities in early stage startups and large companies that could leverage AI. Topics in Advanced Robotic Manipulation. This class is taught in the flipped-classroom format. The student must present an oral thesis proposal and submit the form to their full Reading Committee by Spring Quarter of the fourth year. 3-4 Units. The course is hands on using open source Python packages for working with publicly available quantum processors. Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Program. Introduction to spoken language technology with an emphasis on dialogue and conversational systems. In this course, we focus on 1) establishing why representations matter, 2) classical and moderns methods of forming representations in Computer Vision, 3) methods of analyzing and probing representations, 4) portraying the future landscape of representations with generic and comprehensive AI/vision systems over the horizon, and finally 5) going beyond computer vision by talking about non-visual representations, such as the ones used in NLP or neuroscience. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory. Prerequisites: CS161 or equivalent, STATS116 or equivalent. What role will they play in our system of justice and the practice of law? Computational Biology and Bioinformatics are practiced at different levels in many labs across the Stanford Campus. CS 224N. Same as: LINGUIST 284, SYMSYS 195N. Maintain the 3.6 GPA required for admission to the honors program. 1-9 Unit. Prerequisites: Background in human-centered design (e.g., CS 147, CS 247, ME 115A, or a d.school class) is required. Operating systems design and implementation. For graduate students who are TA-ing an AI course. CS 273C. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. The course is taught in a studio format with in-class discussions and code reviews in addition to lectures. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O. Principles and practices for design and implementation of compilers and interpreters. Recommended: CS164. University requirements for the coterminal master’s degree are described in the “Coterminal Master’s Program” section. Beyond Bits and Atoms - Lab. Satisfy the requirements of one of the following concentrations: 2) Robotics and Mechatronics Concentration. To drop the joint major, students must submit the Declaration or Change of Undergraduate Major, Minor, Honors, or Degree Program. Programming Methodologies in JavaScript and Python. Recursion and recursive data structures (linked lists, trees, graphs). CS 182W. Research project. Cross-Platform Mobile Development. NP-complete? Programming Abstractions. Programming Language Foundations. There will be optional discussion sections on Fridays. School of Engineering . Traditional animation techniques. Human use also examines key challenges of GANs today, including simplification and parametrization are not after... Tools pseudorandomness, such as stacks, queues, sets ) and an open-ended design challenge original project... 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