); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. 1 Unit. 1 Unit. Course transfers are not possible after the bachelor’s degree has been conferred. CS 263. (Previously numbered CS 369G.) Areas of specialization include artificial intelligence, biocomputation, computer and network security, human-computer interaction, information management and analytics, real-world computing, software theory, systems, and theoretical computer science. Deep Learning in Genomics and Biomedicine. Students will each discover a new possible use-case for blockchain and prototype their vision for the future accordingly. Present the thesis at a public colloquium sponsored by the department. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks. CS 348E. 3-4 Units. 3-5 Units. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. Problem-solving Lab for CS109. See also the Undergraduate Advising and Research JMP web site and its associated FAQs. Courses offered by the Department of Computer Science are listed under the subject code CS on the Stanford Bulletin's ExploreCourses web site. Computer Vision: From 3D Reconstruction to Recognition. CS 402L. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. 3 Units. For course descriptions and additional offerings, see the listings in the Stanford Bulletin's ExploreCourses or Bing Overseas Studies. Students may also consult the Student Services Center with questions concerning dropping the joint major. Transition from engineering concepts and theory to real engineering applications. 3-5 Units. See the department's web site for admissions requirements and the application deadline. Can we predict human behavior? Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. Programming Abstractions. (Previously numbered CS376.) Prerequisite: consent of instructor. 3-4 Units. Concepts will be developed in part through guided in-class coding exercises. 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. Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. Recommended: CS 148 and/or 205A. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. 3-4 Units. Select at least three of the following: D. A total of at least 21 units from (A), (B), (C), or the following: Toggle School of Earth, Energy and Environmental Sciences, Handbook for Undergraduate Engineering Programs (UGHB). The student must pass a University oral examination in the form of a defense of the dissertation. Undergrads will need instructor's approval. Advanced Multi-Core Systems. CS 243. Primary focus on enabling students to build apps for both iOS and Android using RN. This course provides a comprehensive introduction to interactive computer graphics, focusing on fundamental concepts and techniques, as well as their cross-cutting relationship to multiple problem domains in interactive graphics (such as rendering, animation, geometry, image processing). Same as: PHIL 356C. 3 Units. 3 Units. Visual Computing Systems. 3 Units. CS 202. No coding involved; however we will be deeply exploring the human operating system. Bridging Policy and Tech Through Design. Haskell is taught and used throughout the course, though much of the material is applicable to other languages. Advanced Operating System Lab: Accelerated. Prerequisite: 110. CS 272. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Over the course of the quarter, we'll explore fundamental techniques in data structure design (isometries, amortization, randomization, word-level parallelism, etc.). Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. In a different vein, convex relaxations are a useful tool for graph partitioning problems; central to the analysis are metric embedding questions for certainly computationally defined metrics. 3 Units. The following core courses fulfill the minor requirements. And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. Doctor of Philosophy [Ph.D.] in Computer Science is a 5-year program. Computers, Ethics, and Public Policy. Then we'll see how those questions are fundamental to a wide variety of recent events from #metoo in tech companies, to the ways the under-representation of women and people of color in tech companies impacts the kinds of products that Silicon Valley brings to market. Classroom instantiation of the Stanford Laptop Orchestra (SLOrk) which includes public performances. CS 499. 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). In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Advanced Topics in Formal Methods. See http://hci.stanford.edu/academics for offerings. 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. CS 359. Open Problems in Coding Theory. Additional Topics in Teaching Computer Science. The Symbolic Systems major (in the School of Humanities and Sciences) offers an opportunity to explore computer science and its relation to linguistics, philosophy, and psychology. Of the 80 graduate programs offered at Stanford University, 11 are offered online or through graduate distance education programs. Select two courses, each from a different area: Select one additional course from the Areas above or from the following: Track Electives: at least three additional courses selected from the Areas and lists above, general CS electives, or the courses listed below. 4 Units. Work in the course consists of reading, class discussion, and practical exercises. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability. 1 Unit. Students will read recent research papers and complete a design project. The course will concretize theories, concepts, and practices in weekly presentations (including examples) from industry experts with significant backgrounds and proven expertise in designing successful, evidence-based, educational technology products. Thus, students needing to take more than two of the courses listed in Requirement 1 actually complete more than 45 units of course work in the program. The mission of the undergraduate program in Management Science and Engineering is to provide students with the fundamentals of engineering systems analysis so that they are able to plan, design, and implement complex economic and technical management systems. 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. CS 194A. Seminar talks by researchers and industry professionals on topics related to modern robotics and autonomous systems. Written analysis and evaluation required. S/NC only; if not appropriate, enroll in CS499. Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. PhD program Students wishing to pursue in-depth research in the field of Probability or Statistics and their applications. These learning outcomes are used in evaluating students and the department's undergraduate program. Students enroll in the CS 294W section attached to the CS 294 project they have chosen. Prerequisites: Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Same as: MED 253. 94305. 3-4 Units. INTERACTIVE MEDIA AND GAMES. Function spaces and functional maps. 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. 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. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. iOS Application Development. Significant parallel programming assignments will be given as homework. Rigorous introduction to Symbolic Logic from a computational perspective. Automated Reasoning: Theory and Applications. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, and the semantic gap between computational models and modern hardware. By precisely asking, and answering such questions of counterfactual inference, we have the opportunity to both understand the impact of past decisions (has climate change worsened economic inequality?) The Social & Economic Impact of Artificial Intelligence. GRE general test scores required. 3 Units. Human-Computer Interaction: Foundations and Frontiers. Same as: AA 222. Students pursuing this joint degree must have at least basic training or experience in computer science. For classes being offered only with an S/NC grading basis (i.e., a letter grade option is not available), students should tell the instructor and Jay Subramanian (CS Ph.D. Student Services Office) at the beginning of the quarter about their desire to use the class to satisfy the Breadth requirement. We will also discuss several application areas where we can apply these techniques. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. CS 247I. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. For the midterm milestone, teams must demonstrate that their routers can interoperate with the other teams by building a small scale datacenter topology. An in-depth treatment of algorithmic and game-theoretic issues in social choice. Phone: 1 (650) 725-3140. For classes that offer a letter grade option, students must take the class for a letter grade and receive a grade of 'A-' or better in order to satisfy the respective Breadth requirement. A. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. This mathematical form is then used by subsequent steps (e.g. (Formerly 223B) An introduction to the concepts and applications in computer vision. This class could also be called "Build an Internet Router": Students work in teams of two to build a fully functioning Internet router, gaining hands-on experience building the hardware and software of a high-performance network system. May be repeated for credit. Students may apply for admission starting this fall. Advanced Systems Laboratory, Accelerated. 3 Units. The course culminates with students forming project teams to create a final video game. 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. 3-4 Units. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. During the fall, we'll build a system that improves on current systems in certain areas: audio quality and latency over spotty Internet connections, video quality and realistic composited scenes with multiple actors, audience feedback, and perhaps digital puppetry. 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. Prerequisite: linear algebra. This project-based class focuses on understanding the use of technology in the world. CS 327A. Classes will cover the basics of patent, trademark, copyright, and trade secret law. degree in Computer Science is intended as a terminal professional degree and does not lead to the Ph.D. degree. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. May be repeated for credit. Focus is on teaching skills, techniques, and final projects grading. Students with little prior experience or those who wish to take more time to study the fundamentals of programming should take CS 106A followed by CS 106B. CS 421. AI has been advancing quickly, with its impact everywhere. CS 103A. Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Same as: MUSIC 128. You will have discussions criticizing papers and assigning grades to them. 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. Contents change each quarter. CS 101. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required. The two majors are identified on the transcript with a notation indicating that the student has completed a "Joint Major.". Select at least four of the following: B. 1 Unit. Class will consist of video tutorials and weekly hands-on lab sections. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. Given class size limitations, an online survey will be used to achieve a diverse class composition. Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. 3-4 Units. in CS degree: The joint MS in Computer Science/MBA degree links two of Stanford University's world-class programs. CS 269Q. This course covers design patterns for social computing and crowdsourcing systems, and the foundational ideas that underpin them. Prerequisites: linear algebra, basic probability and statistics. Case studies and course project. Architectural principles: why the Internet was designed this way? Students work on an existing project of their own or join one of these projects. Faculty advisers guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. 3-4 Units. Same as: ME 320. Research assistants (RAs) help faculty and senior staff members with research in computer science. Worst and average case analysis. Information and application instructions below. Whether you wish to become a start-up founder and CEO; a manager at a tech-centric company; or an individual contributor at Facebook or Google: if you wish to hit the ground running and be highly effective from your first day at work, this course is for you!. Understanding Users. Prerequisites: CS110 or EE102A. CS193Q assumes knowledge of some programming language, and proceeds by showing how each common programming idea is expressed in Python. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. 3-5 Units. CS 154. Not a programming course. This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. 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. Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Research project. 3-4 Units. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. Focus is on Macintosh and Windows operating system maintenance, and troubleshooting through hardware and software foundation and concepts. 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. Introduction to Python Programming. Degrees for a Program. Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. 3-4 Units. Prerequisite: 253 or consent of instructor. Co-requisite: CS103. Prerequisite: CS106A. Analysis of Boolean Functions. CS 335. This course will cover fundamental concepts and principled algorithms in machine learning. Stanford is known for building data science expertise across disciplines. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. CS 331B. 3 Units. The dissertation must be accepted by a reading committee composed of the principal dissertation adviser, a second member from within the department, and a third member chosen from within or outside of the University. CS 309. The student is expected to demonstrate the ability to present scholarly material orally in the dissertation defense. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. CS 106A uses Python as its programming language; CS 106B uses C++. Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Same as: BIODS 237, BIOMEDIN 273B, GENE 236. Computational Biology: Structure and Organization of Biomolecules and Cells. 3 Units. Educational opportunities in high technology research and development labs in the computing industry. The theory group at Stanford invites applications for the Motwani postdoctoral fellowship in theoretical computer science. CS 390C. Prerequisites: AA 228/CS 238 or CS 221. Lectures, reading, and project. 2 Units. They know what dissertation committees Stanford Computer Science Phd Thesis want. Computational Methods for Biomedical Image Analysis and Interpretation. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Pipeline for VR applications unique turning point in human history grading is based on cooperation toward a shared.... With deep learning and interface for human use and used throughout the past decade there has been motivated by applications... 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