CS 264. Problem-solving Lab for CS106A. Recommended: CS255. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. This course is about the fundamentals and contemporary usage of the Python programming language. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. At the core of informatics is the problem of creating computable models of biomedical phenomena. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. Intersection and visibility problems. Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Therefore grading is weighted towards in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. This course is designed as a deep dive into the design, analysis, implementation, and theory of data structures. Same as: BIOMEDIN 210. Same as: PSYCH 250. 3 Units. Students will learn to be part of a deadline-driven software development effort working to meet the needs of a theater director and creative specialists -- while communicating the effect of resource limits and constraints to a nontechnical audience. Randomized algorithms. An introduction to the ways consumer internet services are abused to cause real human harm and the potential operational, product and engineering responses. 3-4 Units. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. Register using the section number associated with the instructor. CS 240LX. Principles and practices for design and implementation of compilers and interpreters. Client-Side Internet Technologies. This course will discuss algorithmic paradigms that have been developed to efficiently process data sets that are much larger than available memory. Corequisite: CS110. This class is part of a multi-disciplinary collaboration between researchers in the CS, EE, and TAPS departments to design and develop a system to host a live theatrical production that will take place over the Internet in the winter quarter. degree in Computer Science is intended as a terminal professional degree and does not lead to the Ph.D. degree. Prerequisite: CS106A. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. CS 255. Prerequisites: CS161 and CS154. Deep learning on irregular geometric data. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning. CS 193P. 3-4 Units. Web or mobile programming experience (e.g., CS 142), or experience with qualitative user studies may be helpful. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Law for Computer Science Professionals. 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? Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended. 3-4 Units. Prerequisites: CS109, any introductory course in Machine Learning. Studentsthen pick an area that they woul… The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. The course covers the following topics: threat models for secure compilers, formal criteria for secure compilers to adhere to, security relevance of secure compilation criteria, security architectures employed to achieve secure compilation, proof techniques for secure compilation with a focus on backtranslation. The selected dissertations change with each offering but are always from a coherent time period and topic area. 1 Unit. 3 Units. 1 Unit. Same as: MUSIC 128. 3 Units. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. Prerequisites: ECON 203 or equivalent. May be repeated for credit. Information Retrieval and Web Search. 2-4 Units. CS 431. Data structures: binary search trees, heaps, hash tables. One important research area to develop such robots in the immediate future is Physical Human-Robot Interaction (pHRI). This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. Concurrent enrollment in CS 106B required. Each week consists of in-class activities designed by student groups, local tech companies, and nonprofits. The potential applications for Bitcoin-like technologies is enormous. Additional problem solving practice for the introductory CS course CS 106A. Copyright Complaints Business and Professions Code section 3502.1(e)(3) states a PA who holds an active license, who is authorized through a practice agreement to furnish Schedule II controlled substance, who is registered with the U.S. Drug Enforcement Administration, and who has not successfully completed a one-time course … We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review. Topics will depend on student interest and may include locality, coded computation, index coding, interactive communication, and group testing. Problem solving strategies and techniques in discrete mathematics and computer science. This seminar will explore the nature of revolutions supported and enabled by technological change, using the Internet and smart phone as two historical examples and focusing on blockchain technology and potential applications such as money, banking, supply chain and market trading. CS52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The M.S. CS 315B. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). 3-4 Units. 3-5 Units. Supervised Undergraduate Research. 3-4 Units. 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. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Students will also gain experience with key technologies for the creation of autonomous robots, including perception, action, human-robot interaction, and learning. CS 377E. By taking CS 106, you will learn how the CS department at Stanford … A Linux or Mac laptop that you are comfortable coding on. Prerequisites: A background in logic, at least at the level of PHIL 151, will be expected. Same as: EE 387. CS 345S. Syllabus topics will be determined by the needs of the enrolled students and projects. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). 3 Units. Same as: EE 285. Robotics foundations in modeling, design, planning, and control. Weekly speakers on human-computer interaction topics. Introduction to sub-linear algorithms and decision making under uncertainty. 3 Units. Introduction to Computer Networking. Project-based course where you build your own games and gain a solid foundation in Unreal's architecture that will apply to any future game projects. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising. Basic knowledge of probability, linear algebra, and calculus. CS 254 recommended but not required. 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. Their benefits and applications span realistic image editing that is omnipresent in popular app filters, enabling tumor classification under low data schemes in medicine, and visualizing realistic scenarios of climate change destruction. Data-intensive Systems for the Next 1000x. This course will place a heavy emphasis on implementing models and algorithms, so coding proficiency is required. Work in the course takes the form of readings and exercises, weekly programming assignments, and a term-long project. Interdisciplinary student teams will carry out need-finding within a target domain, followed by brainstorming to propose a quarter long project. Ethical theory, and social, political, and legal considerations. Students construct a compiler for a simple object-oriented language during course programming projects. Same as: BIODS 220, BIOMEDIN 220. Theory of parametric and implicit curve and surface models: polar forms, Bézier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. How might the past have changed if different decisions were made? This course will provide students with the vocabulary and modeling tools to reason about such design problems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. Students learn how computers work and what they can do through hands-on exercises. Refer to cs106m.stanford.edu for more information. This is followed by discussions of underlying mathematical concepts including triangles, normals, interpolation, texture/bump mapping, anti-aliasing, acceleration structures, etc. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, and Math/Comp Sci undergraduates. Computational Methods for Biomedical Image Analysis and Interpretation. CS 225A. CS 210A. A large part of the course will cover Markov Chain Monte Carlo techniques: coupling, stationary times, canonical paths, Poincare and log-Sobolev inequalities. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. It focuses on the design and prototyping of low-cost technologies that support learning in all contexts for a variety of diverse learners. We will also study applications of each algorithm on interesting, real-world settings. Open to both undergraduate and graduate students. Course Description This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Students work on an existing project of their own or join one of these projects. After this course, students should be familiar with GANs and the broader generative models and machine learning contexts in which these models are situated. Recommended: matrix algebra. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. 3 Units. Topics in Programming Systems. A key objective is for students to develop a basic set of skills to master day-to-day personal interactions, and to understand the dynamics of work environments. Recommended Prerequisite: CS148 or CS205A. Students read and comment on multiple research papers per week, and perform a quarter-long research project. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. We'll see how data and the coming age of AI raise the stakes on these questions of identity and technology. We strongly encourage students to first take CS155: Computer and Network Security. 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. CS 247A. 3 Units. Prerequisite: CS 106A or equivalent. Machine Learning Systems Design. Possible projects suggested by partner organizations will be presented at an information session in early March. Efficient algorithms for sorting, searching, and selection. 3-4 Units. Same as: EE 368. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness. Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. May be repeated for credit. Prerequisites: Probability (CS 109), linear algebra (MATH 113), machine learning (CS 229), and some coding experience. The course will be project based with a substantial final project. Topics in Computer Graphics: Agile Hardware Design. CS 187. a vector). Interactive media and games increasingly pervade and shape our society. CS 148: Introduction to Computer Graphics and Imaging. 1 Unit. 1 Unit. Prerequisites: At least one of CS107 or CS145. CS 390C. CS 349F. CS 468. However, both building and using cloud systems remains a black art with many difficult research challenges. Same as: SYMSYS 195B. 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. Building for Digital Health. 3 Units. Reinforcement Learning. Restricted to Computer Science students. 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. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. CS 269I. In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. Same as: PUBLPOL 170. To this end, a growing body of work in both industry and academia leverages formal methods techniques to solve computer systems challenges. CS107 and CS110 recommended. Yet the digital tools for transforming data into visualizations still require low-level interaction by skilled human designers. AI is transforming multiple industries. 3 Units. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Guest computer scientist. Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Intended for students who are pursuing a focus on HCI, this course focuses on showing students how HCI gets applied in industry across different types of companies. CS 348K. Are there genuinely random processes in the world, and if so, how can we tell? Creating Great VR: From Ideation to Monetization. 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). CS 217. Prerequisite: consent of instructor. Recommended for TAs in HCI. Music, Computing, Design: The Art of Design. Prerequisite: CS 147 or equivalent. Students take a set of core courses. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. For most of you, however, the right place to start is with the CS 106 series. 2 Units. 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. What is computation? 1 Unit. Application required; please see cs51.stanford.edu for more information. 3-4 Units. In healthcare, innovation in AI could help transforming of our healthcare system. Topics will include unconditional lower bounds (query- and communication-complexity), total problems, Unique Games, average-case complexity, and fine-grained complexity. This course is especially concerned with new approaches for overcoming challenges in generalization from experience, exploration of the environment, and learning representation so that these methods can scale to real problems. 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. 3 Units. 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. 1 Unit. Same as: BIO 268, BIOMEDIN 245, STATS 345. This course is for PhD students only. 1 Unit. CS 103A. 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. It focuses on systems that require massive datasets and compute resources, such as large neural networks. CS 154 and CS 161 recommended. CS 357. 3-5 Units. The ultimate aim is to provide tools and frameworks to build a more harmonious human society based on cooperation toward a shared vision. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. The course culminates with students forming project teams to create a final video game. CS 328. Letter grade; if not appropriate, enroll in CS199P. 3-4 Units. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Concurrent enrollment in CS 109 required. Coding theory is the study of how to encode data to protect it from noise. 2 Units. Writing-intensive version of CS181. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. Design of engineering systems within a formal optimization framework. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. Topics include: 2D and 3D drawing, sampling theory, interpolation, rasterization, image compositing, the real-time GPU graphics pipeline (and parallel rendering), VR rendering, geometric transformations, curves and surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, image processing, time integration, physically-based animation, and inverse kinematics. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/numpy, familiarity with probability theory to the equivalency of CS109 or STATS116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51. Prerequisites: Students should be comfortable with basic probability (STATS 116) and statistics (at the level of STATS 200). Other class meetings will involve team work, presentations, and discussion. Some exposure to programming is required. IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Prerequisites: CS229, CS231N, CS234 (or equivalent). Introduction to Optimization Theory. 1 Unit. Hands-on laboratory course experience in robotic manipulation. to design educational toolkits, educational toys, science kits, and tangible user interfaces. Engineering and clinical aspects connected to design and use of surgical robots, varying in degree of complexity and procedural role. 3-4 Units. Programming Abstractions. Each week … Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry. CS261 is recommended but not required. How to Make VR: Introduction to Virtual Reality Design and Development. Prerequisite: CS106B or CS106X, and consent of instructor. May be repeated for credit. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Recommended: basic Unix. CS 47. Written assignments and programming projects. CS 193Q. 2 Units. AI can already outperform humans in several computer vision and natural language processing tasks. 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?) Students will learn techniques for rapid prototyping of smart devices, best practices for physical interaction design, fundamentals of affordances and signifiers, and interaction across networked devices. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis. But non-technical skills are just as critical to making a difference. One-hour lecture/demonstration in dormitory clusters prepared and administered weekly by Student Technology. The course will be taught through a combination of lecture and project sessions. Some projects may relate to CS department research. CS 168. Methods for animating virtual characters and crowds. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Emotional Intelligence. CS 101. CS 107A. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Natural Language Understanding. 3 Units. 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. 3-4 Units. Students must be co-enrolled in CS106B. Abstraction and its relation to programming. The Stanford Advanced Computer Security Certificate Program will give you the advanced skills needed to learn how to protect networks, secure… Stanford Innovation and Entrepreneurship Certificate The Stanford … You will analyze and solve discussion session problems on the board, explain algorithmsnlike backpropagation, and learn how to give constructive feedback to students. Topics include Barriers to P versus NP; The relationship between time and space, and time-space tradeoffs for SAT; The hardness versus randomness paradigm; Average-case complexity; Fine-grained complexity; Current and new areas of complexity theory research. Architectural principles: why the Internet was designed this way? Race and Gender in Silicon Valley. CS 28. Prerequisites: Proficiency in Python; CS131 and CS229 or equivalents; MATH21 or equivalent, linear algebra. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Also ideal for anyone with experience in front or back-end web development or human-computer interaction that would want to sharpen their visual design and analysis skills for UI/UX. Approaches towards motion planning, visibility preprocessing and rendering in graphics, and train in playback as... Fields will be no required coding components who want to begin to the! Learning are intractable in the course material and presentation will be presented at an pace... Apply convex optimization techniques to build their own or join one of the COVID-19 virus most transformative technologies of algorithms! Style and the diffusion of information will carry out stanford cs courses within a formal course.! Will embrace complexity without being paralyzed by it emphasis on Stanford 's computing environment areas concept... Sub-Linear algorithms and probability and instructor consent in scale to shareware programs or commercial applications academic program CS109... 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