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361 - 366 of 366 results for: CS

CS 523: Research Seminar in Computer Vision + X

With advances in deep learning, computer vision (CV) has been transforming all sorts of domains, including healthcare, human-computer interaction, transportation, art, sustainability, and so much more. In this seminar, we investigate its far-reaching applications, with a different theme chosen as the focus each quarter (e.g. the inaugural quarter was CV + Healthcare; the theme for the quarter will be listed on the class syllabus). Throughout the quarter, we deeply examine these themes in CV + X research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and other domains. 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 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).
Last offered: Spring 2022 | Units: 1-2

CS 524: AI for Science

AI is transforming scientific discovery - from protein structures prediction, to new materials discovery, to plasma control. This graduate-level seminar surveys the emerging discipline of AI for Science, emphasizing the recurring ML-theoretic themes most salient to scientific regimes: e.g., multi-scale modeling, multi-modal architectures, differentiable simulation, and inference - time reasoning. Weekly meetings pair a classical milestone (e.g., AlphaFold, AlphaGo) with a recent breakthrough (e.g., GNoME, GraphCast, TAE's Optometrist algorithm). Students read, present, and critique papers, replicate key results in lightweight coding labs, and pursue an open - ended research project targeting new science or tooling.
Terms: Aut | Units: 3
Instructors: Davis, J. (PI) ; Leskovec, J. (PI)

CS 525: Data for AI

CS525 surveys the landscape of data for AI with a focus on contemporary learning problems such as training language models or multimodal models. Students will learn about important datasets and common data processing methods including filtering and deduplication. Further topics will include synthetic data, data attribution, and environments for reinforcement learning. The course will also cover ethical and legal aspects of training data such as copyright and privacy. Over the course of the class, students will build a training set for a learning problem of their choice. The class will consist of faculty lectures, student presentations, and guest lectures.
Terms: Win | Units: 3
Instructors: Schmidt, L. (PI)

CS 528: Machine Learning Systems Seminar

Machine learning is driving exciting changes and progress in computing systems. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? What challenges does industry face when deploying machine learning systems in the real world, and how can new system designs meet those challenges? In this weekly talk series, we will invite speakers working at the frontier of machine learning systems, and focus on how machine learning changes the modern programming stack. Topics will include programming models for ML, infrastructure to support ML applications such as ML Platforms, debugging, parallel computing, and hardware for ML. May be repeated for credit.
Last offered: Autumn 2024 | Units: 1 | Repeatable 3 times (up to 3 units total)

CS 547: Human-Computer Interaction Seminar

Weekly speakers on human-computer interaction topics. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit
Instructors: Subramonyam, H. (PI) ; Wodtke, C. (PI) ; Yang, D. (PI) ; Haghighi, N. (TA) ; Ziems, C. (TA)

CS 802: TGR Dissertation

Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
Instructors: Achour, S. (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) ; Charikar, M. (PI) ; Choi, Y. (PI) ; Dror, R. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fan, J. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Finn, C. (PI) ; Follmer, S. (PI) ; Fox, E. (PI) ; Fox, J. (PI) ; Ganguli, S. (PI) ; Genesereth, M. (PI) ; Goel, A. (PI) ; Goodman, N. (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) ; Kennedy, M. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (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) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Pavone, M. (PI) ; Pea, R. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Raina, P. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Rosenblum, M. (PI) ; Rubinstein, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Schmidt, L. (PI) ; Sidford, A. (PI) ; Song, S. (PI) ; Subramonyam, H. (PI) ; Tan, L. (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) ; Zhandry, M. (PI) ; Zou, J. (PI)
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