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351 - 360 of 366 results for: CS

CS 476A: Music, Computing, Design: The Art of Design (MUSIC 256A)

This course explores the artful design of software tools, toys, games,ninstruments, and experiences. Topics include programming, audiovisualndesign, strategies for crafting interactive systems, game design, asnwell as aesthetic and social considerations of shaping technology in ournworld today. Course work features several programming assignments withnan emphasis on critical design feedback, reading responses, and an"design your own" final project. Prerequisite: experience in C/C++/Javanor Unity/C#.  See https://ccrma.stanford.edu/courses/256a/
Terms: Aut | Units: 3-4
Instructors: Wang, G. (PI) ; Murakami, E. (TA)

CS 486: Advanced Large Language Models Research Seminar

This research seminar builds upon the theoretical foundations established in the lecture-based LLM courses, providing students with hands-on experience in conducting original research at the frontier of Large Language Model advancement toward AGI capabilities. Students will design, implement, and evaluate novel approaches to enhance LLM reasoning, planning, and multi-agent collaboration.
Terms: Spr | Units: 3
Instructors: Chang, E. (PI)

CS 498C: Introduction to CSCL: Computer-Supported Collaborative Learning (EDUC 315A)

This seminar introduces students to foundational concepts and research on computer-supported collaborative learning (CSCL). It is designed for LSTD doctoral students, LDT masters' students, other GSE graduate students and advanced undergraduates inquiring about theory, research and design of CSCL. CSCL is defined as a triadic structure of collaboration mediated by a computational artefact (participant-artifact-participant). CSCL encompasses two individuals performing a task together in a short time, small or class-sized groups, and students following the same course, digitally interacting.
Terms: Win | Units: 3
Instructors: Pea, R. (PI) ; Pittman, J. (TA)

CS 498D: Design for Learning: Generative AI for Collaborative Learning (DESIGN 292, EDUC 449)

Would you like to design ways to use generative AI to help humans learn with other humans? In this course, you will develop creative ways to use generative AI to support collaborative learning, also learning more about AI as researchers continue to improve tools like ChatGPT. In creating new learning activities that could be used at Stanford or in other courses, you will build experience with fundamentals of design, including the design abilities of learning from others, navigating ambiguity, synthesizing information, and experimenting rapidly. You will do this by tackling real design challenges presented by our project partners, which include several Stanford programs, while drawing on your own first-hand experience as students. This class is open to all students, undergraduate and graduate, of any discipline. No previous design experience or experience with AI is required. Just a collaborative spirit and hard work.
Last offered: Autumn 2023 | Units: 3

CS 499: Advanced Reading and Research

Letter grade only. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. Letter grade; if not appropriate, enroll in CS499P.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: Achour, S. (PI) ; Adeli, E. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) ; Akbarpour, M. (PI) ; Altman, R. (PI) ; Anari, N. (PI) ; Barrett, C. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Boahen, K. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Bouland, A. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Charikar, M. (PI) ; Choi, Y. (PI) ; Dally, B. (PI) ; Dauterman, E. (PI) ; Dror, R. (PI) ; Duchi, J. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fan, J. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Finn, C. (PI) ; Fogg, B. (PI) ; Follmer, S. (PI) ; Fox, E. (PI) ; Fox, J. (PI) ; Ganguli, S. (PI) ; Genesereth, M. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guestrin, C. (PI) ; Guibas, L. (PI) ; Haber, N. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hayden, P. (PI) ; Hennessy, J. (PI) ; Ho, D. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kennedy, M. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Linderman, S. (PI) ; Liu, K. (PI) ; Ma, T. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McClelland, J. (PI) ; McKeown, N. (PI) ; Mirhoseini, A. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Montanari, A. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Pea, R. (PI) ; Piech, C. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Raina, P. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Rosenblum, M. (PI) ; Rubinstein, A. (PI) ; Saberi, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schmidt, L. (PI) ; Schwarz, K. (PI) ; Sidford, A. (PI) ; Song, S. (PI) ; Subramonyam, H. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Utterback, C. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zelenski, J. (PI) ; Zhandry, M. (PI) ; Zou, J. (PI)

CS 499P: Advanced Reading and Research

Graded satisfactory/no credit. Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS199, masters students should enroll in CS399. S/NC only; if not appropriate, enroll in CS499.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: Achour, S. (PI) ; Adeli, E. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) ; Akbarpour, M. (PI) ; Altman, R. (PI) ; Anari, N. (PI) ; Barrett, C. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Boahen, K. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Bouland, A. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Charikar, M. (PI) ; Choi, Y. (PI) ; Dally, B. (PI) ; Dauterman, E. (PI) ; Dror, R. (PI) ; Duchi, J. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fan, J. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Finn, C. (PI) ; Fogg, B. (PI) ; Follmer, S. (PI) ; Fox, E. (PI) ; Fox, J. (PI) ; Ganguli, S. (PI) ; Genesereth, M. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guestrin, C. (PI) ; Guibas, L. (PI) ; Haber, N. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hayden, P. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kennedy, M. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Linderman, S. (PI) ; Liu, K. (PI) ; Ma, T. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McClelland, J. (PI) ; McKeown, N. (PI) ; Mirhoseini, A. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Pea, R. (PI) ; Piech, C. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Raina, P. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Rosenblum, M. (PI) ; Rubinstein, A. (PI) ; Saberi, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schmidt, L. (PI) ; Schwarz, K. (PI) ; Sidford, A. (PI) ; Song, S. (PI) ; Subramonyam, H. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Utterback, C. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zelenski, J. (PI) ; Zhandry, M. (PI) ; Zou, J. (PI)

CS 520: Knowledge Graphs

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar and will include lectures on knowledge graph topics (e.g., data models, creation, inference, access) and invited lectures from prominent researchers and industry practitioners. The seminar emphasizes synthesis of AI, database systems and HCI in creating integrated intelligent systems centered around knowledge graphs.
Last offered: Spring 2022 | Units: 1

CS 521: Seminar on AI Safety

In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. We will explore some of the major problems in this area from the viewpoint of industry and academia. 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. Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended.
Last offered: Spring 2025 | Units: 1

CS 522: Seminar in Artificial Intelligence in Healthcare

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. 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. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at https://tinyurl.com/cs522-stanford
Terms: Aut | Units: 1
Instructors: Dror, R. (PI)

CS 523: Research Seminar in Computer Vision and Healthcare

With advances in deep learning, computer vision (CV) has been transforming healthcare, from diagnosis to prognosis, from treatment to prevention. Its far-reaching applications include surgical assistants, patient monitoring, data synthesis, and cancer screening. Before these algorithms make their way into the clinic, however, there is exciting research to develop methods that are accurate, robust, interpretable, grounded, and human-centered. In this seminar, we deeply examine these themes in medical CV research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and healthcare. Each week students will read and prepare questions and reflections on an assigned paper authored by that week's speaker. We highly encourage students who are interested in taking an interactive, deep dive into medical CV research literature to apply. While there are no hard requirements, we strongly suggest having the background and fluency necessary to read and analyze AI research papers (thus MATH 51 or linear algebra, and at least one of CS 231x, 224x, 221, 229, 230, 234, 238, AI research experience for CV and AI fundamentals may be helpful).
| Units: 1
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