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81 - 90 of 99 results for: CS ; Currently searching spring courses. You can expand your search to include all quarters

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CS 355: Advanced Topics in Cryptography

Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. May be repeated for credit. Prerequisite: CS255.
Terms: Spr | Units: 3 | Repeatable for credit
Instructors: Datta, T. (PI) ; Nguyen, W. (PI) ; Partap, A. (PI) ; Miranda, N. (TA)

CS 360: Simplicity and Complexity in Economic Theory (ECON 284)

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.
Terms: Spr | Units: 3-5
Instructors: Akbarpour, M. (PI)

CS 361: Engineering Design Optimization (AA 222)

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.
Terms: Spr | Units: 3-4

CS 372: Artificial Intelligence for Precision Medicine and Psychiatric Disorders

Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta l more »
Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.
Terms: Spr | Units: 3
Instructors: Chang, E. (PI) ; Jin, M. (TA)

CS 377Q: Designing for Accessibility (ME 214)

Designing for accessibility is a valuable and important skill in the UX community. As businesses are becomeing more aware of the needs and scope of people with some form of disability, the benefits of universal design, where designing for accessibility ends up benefiting everyone, are becoming more apparent. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility through individual assignments. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability. This class focuses on the accessibility of UX with computers, mobile phones, VR, and has a design class prerequisite (e.g., CS147, ME115A).
Terms: Spr | Units: 3-4
Instructors: Tang, J. (PI)

CS 377U: Understanding Users

This project-based class focuses on understanding the use of technology in the world. Students will learn generative and evaluative research methods to explore how systems are appropriated into everyday life in a quarter-long project where they design, implement and evaluate a novel mobile application. Quantitative (e.g. A/B testing, instrumentation, analytics, surveys) and qualitative (e.g. diary studies, contextual inquiry, ethnography) methods and their combination will be covered along with practical experience applying these methods in their project. Prerequisites: CS 147, 193A/193P (or equivalent mobile programming experience).
Terms: Spr | Units: 3-4
Instructors: Bentley, F. (PI)

CS 390A: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Achour, S. (PI) ; 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) ; Blikstein, P. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Borenstein, J. (PI) ; Bouland, A. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Chang, M. (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) ; Fischer, M. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Follmer, S. (PI) ; Fox, A. (PI) ; Fox, E. (PI) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guestrin, C. (PI) ; Guibas, L. (PI) ; Haber, N. (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) ; Kennedy, M. (PI) ; Khatib, O. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Lee, C. (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) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Montgomery, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Pande, V. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Pea, R. (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) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schramm, T. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Sidford, A. (PI) ; Sosic, R. (PI) ; Stanford, J. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Troccoli, N. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wodtke, C. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 390B: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: Achour, S. (PI) ; 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) ; Blikstein, P. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Borenstein, J. (PI) ; Bouland, A. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Chang, M. (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) ; Fogg, B. (PI) ; Follmer, S. (PI) ; Fox, A. (PI) ; Fox, E. (PI) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guestrin, C. (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) ; Khatib, O. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Lee, C. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Ma, T. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Pea, R. (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) ; Rubinstein, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schramm, T. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Sidford, A. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Troccoli, N. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 390C: Curricular Practical Training

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. CS 390A, CS390B, and CS390C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: A. Hudson, D. (PI) ; Achour, S. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Barrett, C. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (PI) ; Borenstein, J. (PI) ; Bouland, A. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; Dill, D. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Finn, C. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Follmer, S. (PI) ; Fox, A. (PI) ; Fox, E. (PI) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Khatib, O. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Lam, M. (PI) ; Latombe, J. (PI) ; Lee, C. (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) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Okamura, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Pea, R. (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) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Sidford, A. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Troccoli, N. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Wang, G. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wu, J. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI)

CS 390D: Part-time Curricular Practical Training

For qualified computer science PhD students only. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science PhD students engage in research and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform. Students on F1 visas should be aware that completing 12 or more months of full-time CPT will make them ineligible for Optional Practical Training (OPT).
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit
Instructors: Achour, S. (PI) ; 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) ; Bouland, A. (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) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (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) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Khatib, O. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (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) ; 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) ; Pande, V. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Piech, C. (PI) ; Plotkin, S. (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) ; Rubinstein, A. (PI) ; Saberi, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yan, L. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)
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