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Due to recent announcements about Autumn Quarter (see the President's update), please expect ongoing changes to the class schedule.

291 - 300 of 310 results for: CS

CS 448M: Making Making Machines for Makers

An introductory, project-based exploration of systems and processes for making things using computer-aided design and manufacturing, and an introduction to machines and machine tools. Emphasis will be placed on building novel machines and related software for use by "makers" and interactive machines. Course projects will encourage students to understand, build and modify/hack a sequence of machines: (1) an embroidery machine for custom textiles, (2) a paper cutting machine (with drag knife) for ornamental design, and (3) an XY plotter with Arduino controller. Through these projects students explore both (i) principles of operation (mechanical, stepper motors and servos, electrical control, computer software), and (ii) computer algorithms (trajectory, tool path, design). Current trends in interactive machines will be surveyed. The course will culminate in a final student-selected project. Prerequisite: CS106A or equivalent programming experience. Students should have a desire to make things.
Terms: Spr | Units: 3-4

CS 448P: Hacking the Pandemic

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. In addition to brief lectures, guest speakers, and moderated discussions and brainstorming sessions, the course will primarily consist of self-organized team projects where students find creative ways to contribute by leveraging any and all computational tools at our disposal (e.g., algorithms, app development, HCI, remote interaction and communication, data visualization, modeling and simulation, fabrication and 3d printing, design, computer games, VR, computer systems and networking, AI, statistics, bioinformatics, etc.). Prerequisite: CS106B.
Last offered: Spring 2020

CS 448V: Topics in Computer Graphics: Computational Video Manipulation

The goal of this graduate (advanced undergraduate also welcome) course is to survey recent work on computational video analysis and manipulation techniques. We will learn how to acquire, represent, edit and remix video. Several popular video manipulation algorithms will be presented, with an emphasis on using these techniques to build practical systems. Students will have the opportunity to acquire their own video and implement the processing tools needed to computationally analyze and manipulate it. The course will be project based with a substantial final project.
Last offered: Spring 2020

CS 468: Topics in Geometric Algorithms: Non-Euclidean Methods in Machine Learning

Contents of this course vary with each offering. Past offerings have included geometric matching, surface reconstruction, collision detection, computational topology., differential geometry for computer scientists, computational symmetry and regularity, and data-driven shape analysis. The 2020-21 offering will be on Non-Euclidean Methods in Machine Learning. May be repeated for credit.nPrerequisites: Math 51 and 52 or equivalent, basic coding.
Terms: Aut | Units: 3 | Repeatable for credit

CS 472: Data science and AI for COVID-19 (BIODS 472, BIOMEDIN 472)

This project class investigates and models COVID-19 using tools from data science and machine learning. We will introduce the relevant background for the biology and epidemiology of the COVID-19 virus. Then we will critically examine current models that are used to predict infection rates in the population as well as models used to support various public health interventions (e.g. herd immunity and social distancing). The core of this class will be projects aimed to create tools that can assist in the ongoing global health efforts. Potential projects include data visualization and education platforms, improved modeling and predictions, social network and NLP analysis of the propagation of COVID-19 information, and behavior-nudging tools. The class is aimed toward students with experience in data science and AI, and will include guest lectures by biomedical experts. Prerequisites: background in machine learning and statistics ( CS229, STATS216 or equivalent). Some biological background is helpful but not required.
Last offered: Spring 2020

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

Creative design for computer music software. Programming, audiovisual design, as well as software design for musical tools, instruments, toys, and games. Provides paradigms and strategies for designing and building music software, with emphases on interactive systems, aesthetics, and artful product design. Course work includes several programming assignments and a "design+implement" final project. Prerequisite: experience in C/C++ and/or Java.See https://ccrma.stanford.edu/courses/256a/
Terms: Aut | Units: 3-4

CS 476B: Music, Computing, Design II: Virtual and Augmented Reality for Music (MUSIC 256B)

Aesthetics, design, and exploration of creative musical applications of virtual reality (VR) and augmented reality (AR), centered around VR and mobile technologies. Comparison between AR, VR, and traditional software design paradigms for music. Topics include embodiment, interaction design, novel instruments, social experience, software design + prototyping. Prerequisite: MUSIC 256A / CS 476A.
Last offered: Winter 2016

CS 481: Digital Technology and Law: Foundations

Taught by a team of law and engineering faculty, this unique interdisciplinary course will empower students across the University to work together and exercise leadership on critically important debates at the intersection of law and digital technology. 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). Substantively, the course will be organized into modules focused on distinct law-tech intersections, including: platform regulation, speech, and intermediary liability; algorithmic bias and civil rights; autonomous systems, safety, and tort liability; "smart" contracting; data privacy and consumer protection; "legal tech," litigation, and acc more »
Taught by a team of law and engineering faculty, this unique interdisciplinary course will empower students across the University to work together and exercise leadership on critically important debates at the intersection of law and digital technology. 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). Substantively, the course will be organized into modules focused on distinct law-tech intersections, including: platform regulation, speech, and intermediary liability; algorithmic bias and civil rights; autonomous systems, safety, and tort liability; "smart" contracting; data privacy and consumer protection; "legal tech," litigation, and access to justice; government use of AI; and encryption and criminal procedure. 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. Law students will emerge from the course with a basic understanding of core digital technologies and related legal frameworks and a roadmap of curricular and career pathways one might follow to pursue each area further. Students from elsewhere in the University, from engineering to business to the social sciences and beyond, will emerge with an enhanced capacity to critically assess the legal and policy implications of new digital technologies and the ways society can work to ensure those technologies serve the public good. All students will learn to work together across disciplinary divides to solve technical, legal, and practical problems. There are no course prerequisites, and no prior legal or technical training will be assumed. Students will be responsible for short discussion papers or a final paper. After the term begins, students electing the final paper option can transfer from section 1 to section 2, which meets the R requirement, with consent of the instructor. This class is cross-listed in the University and undergraduates and graduates are eligible to take it. Consent Application for Non-Law Students: We will try to accommodate all students interested in the course. But to facilitate planning and confirm interest, please fill out a consent application ( https://forms.gle/hLAQ7JUm2jFTWQzE9) by March 13, 2020. Applications received after March 13 will be considered on a rolling basis. Elements used in grading: Attendance, Class Participation; Written Assignments or Final Paper.
Last offered: Spring 2020

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: Agrawala, M. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Anari, N. (PI) ; Bailis, P. (PI) ; Barrett, C. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Duchi, J. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Finn, C. (PI) ; Fisher, K. (PI) ; Follmer, S. (PI) ; Fox, A. (PI) ; Fox, J. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (PI) ; Goodman, N. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Liu, K. (PI) ; Ma, T. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Montanari, A. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Piech, C. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Rubinstein, A. (PI) ; Saberi, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Sidford, A. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Ullman, J. (PI) ; Utterback, C. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yan, L. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 499P: Advanced Reading and Research

Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit
Instructors: Agrawala, M. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Bailis, P. (PI) ; Barrett, C. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Duchi, J. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Finn, C. (PI) ; Fisher, K. (PI) ; Follmer, S. (PI) ; Fox, A. (PI) ; Fox, J. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (PI) ; Goodman, N. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Liu, K. (PI) ; Ma, T. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Piech, C. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Rubinstein, A. (PI) ; Saberi, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Ullman, J. (PI) ; Utterback, C. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Wang, G. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yan, L. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)
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