Prerequisites: CS 107, MATH 51. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. CS 223A. Syllabus topics will be determined by the needs of the enrolled students and projects. 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. Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. User Interface Design Project. Prerequisites: Programming maturity and comfort with math proofs, at the levels of CS107 and CS103. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Prerequisite: 253 or consent of instructor. Curricular Practical Training. Extracting meaning, information, and structure from human language text, speech, web pages, social networks. This course is about the study of boolean functions from a complexity-theoretic perspective, with an emphasis on analytic methods. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent. Modeling Biomedical Systems. Same as: LINGUIST 284, SYMSYS 195N. 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). You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. Recommended: CS 142 and/or CS 221. Through creating their own games each week, students explore topics including 2D/3D Art, Audio, User Interface design, Production, Narrative Design, Marketing, and Publishing. Same as: SYMSYS 195V. 3-4 Units. The final class mini-project consists of building out a ray tracer to create visually compelling images. CS 109A. Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. Topics vary. Deep Multi-task and Meta Learning. Prerequisite: discrete algorithms at the level of CS161. CS 448B. May be repeated for credit. Computational Education. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisite: CS110. CS 499. Designing Solutions to Global Grand Challenges. Recommended: CS248. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. No prerequisite for PhD students or students outside of CS and Symbolic Systems. 3-5 Units. CS 273A. Recommended: CS 131, 223A, 229 or equivalents. Prerequisite: successful completion of CS198. Restricted to Computer Science students. 1 Unit. Computer Vision and Image Analysis of Art. You want to be the brain, too. In this project-based course, one meeting per week will bring in new information, including visiting experts. Same as: Accelerated. Aesthetics, design, and exploration of creative musical applications of virtual reality (VR) and augmented reality (AR), centered around VR and mobile technologies. As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. We plan to have a weekly seminar speaker to discuss issues such as verification of AI systems, reward misalignment and hacking, secure and attack-resilient AI systems, diagnosis and repair, issues regarding policy and ethics, as well as the implications of AI safety in automotive industry. 1 Unit. Can randomness help us answer difficult questions more accurately or more efficiently? We will explore some of the major problems in this area from the viewpoint of industry and academia. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Digital Image Processing. 3-4 Units. Prerequisite: CS 107 or equivalent. The class involves team design projects and prototyping. CS 225A. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. CS 472. Qualified computer science students engage in internship work and integrate that work into their academic program. How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? nThe focus of CS247g is an introduction to theory and practice of the design of games. Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics in Computer Networks. Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Exploring Computational Journalism. Designing for Accessibility. 3 Units. Prerequisites: Students should be comfortable with basic probability (STATS 116) and statistics (at the level of STATS 200). The complexity of modern computer systems requires rigorous and systematic verification/validation techniques to evaluate their ability to correctly and securely support application programs. CS 124. 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. CS229) and basic neural network training tools (eg. Prerequisites: 103 or 103B, and 107. The Blockchain Revolution Will Not Be Televised. This course is for PhD students only. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. CS 141. 1-5 Unit. You will undoubtedly leave Stanford with the technical skills to excel in your first few jobs. Specific topics to be announced per-quarter. Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Same as: MS&E 213. Prerequisites: CS110 for undergraduates, EE282 for graduate students. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats. Provides paradigms and strategies for designing and building music software, with emphases on interactive systems, aesthetics, and artful product design. CS 193U. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. Detailed reading of 5 selected Ph.D. dissertations within a field of computer science. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. Run as a hands-on studio class, students will design and prototype games for social change and civic engagement. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Advanced Reading and Research. The class will also include a guest speaker who will give teaching advice and talk about AI. 1-3 Unit. Additional problem solving practice for CS103. Computational Biology in Four Dimensions. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. 3-4 Units. Starting January 4, 2021, enrollment will open to all students if additional spaces remain available in the class. This lab course is a hands-on introduction to the prototyping and fabrication of tangible, interactive technologies, with a special focus on learning and education. Prerequisite: consent of instructor. After reviewing recent work in AI that has leveraged ideas from logic, we will slow down and study in more detail various components of high-level intelligence and the tools that have been designed to capture those components. CS 369M. 3-4 Units. Advanced Survey of Reinforcement Learning. Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. CS 224S. The focus of CS247S is Service Design. CS 375. The course will involve several in-depth programming assignments and a self-selected final project that explores concepts covered in the class. Prerequisites: CS107 and CS161, or permission from the instructor. Prerequisite: Completion of at least 135 units and consent of instructor. Prerequisite: CS 147 or equivalent. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. CS 236G. Technology for Financial Systems. INTERACTIVE MEDIA AND GAMES. Main class components are workshops, community discussions, guest speakers and mentorship. Prerequisites: Background in human-centered design (e.g., CS 147, CS 247, ME 115A, or a d.school class) is required. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. 1 Unit. This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams. Fundamentals: homogeneous coordinates and transformation. 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