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231 - 240 of 259 results for: CS

CS 395: Independent Database Project

For graduate students in Computer Science. Use of database management or file systems for a substantial application or implementation of components of database management system. Written analysis and evaluation required. Register using the section number associated with the instructor. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Aiken, A. (PI) ; Altman, R. (PI) ; Baker, M. (PI) ; Barbagli, F. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Boneh, D. (PI) ; Bradski, G. (PI) ; Brafman, R. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; De-Micheli, G. (PI) ; Dill, D. (PI) ; Dwork, C. (PI) ; Engler, D. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Heer, J. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Johari, R. (PI) ; Johnson, M. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Klemmer, S. (PI) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (PI) ; Kozyrakis, C. (PI) ; Lam, M. (PI) ; Latombe, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Motwani, R. (PI) ; Musen, M. (PI) ; Nass, C. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Nilsson, N. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Parlante, N. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Shoham, Y. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Van Roy, B. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Winograd, T. (PI) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 399: Independent Project

Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Letter (ABCD/NP)
Instructors: Agrawala, M. (PI) ; Aiken, A. (PI) ; Akeley, K. (PI) ; Altman, R. (PI) ; Baker, M. (PI) ; Barbagli, F. (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) ; Boyd, S. (PI) ; Bradski, G. (PI) ; Brafman, R. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Casado, M. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Cooper, S. (PI) ; Dally, B. (PI) ; De-Micheli, G. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Durumeric, Z. (PI) ; Dwork, C. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goel, S. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Heer, J. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Johnson, M. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Klemmer, S. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (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) ; MacCartney, B. (PI) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Montgomery, S. (PI) ; Motwani, R. (PI) ; Musen, M. (PI) ; Nass, C. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Nilsson, N. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Pande, V. (PI) ; Parlante, N. (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) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Sidford, A. (PI) ; Socher, R. (PI) ; Sosic, R. (PI) ; Stepp, M. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Williams, V. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wodtke, C. (PI) ; Wootters, M. (PI) ; Yamins, D. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 399P: Independent Project

Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Agrawala, M. (PI) ; Aiken, A. (PI) ; Akeley, K. (PI) ; Altman, R. (PI) ; Bailis, P. (PI) ; Baker, M. (PI) ; Barbagli, F. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (PI) ; Boyd, S. (PI) ; Bradski, G. (PI) ; Brafman, R. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Casado, M. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Cooper, S. (PI) ; Dally, B. (PI) ; De-Micheli, G. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Dwork, C. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Heer, J. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Johnson, M. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Klemmer, S. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (PI) ; Kozyrakis, C. (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) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McKeown, N. (PI) ; Mitchell, J. (PI) ; Motwani, R. (PI) ; Musen, M. (PI) ; Nass, C. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Nilsson, N. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Parlante, N. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Shoham, Y. (PI) ; Socher, R. (PI) ; Stepp, M. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Wang, G. (PI) ; Wetzstein, G. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Williams, V. (PI) ; Winograd, T. (PI) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 402: Beyond Bits and Atoms: Designing Technological Tools (EDUC 236)

Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 402L: Beyond Bits and Atoms - Lab (EDUC 211)

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. (No prior prototyping experience required.) It focuses on the design and prototyping of low-cost technologies that support learning in all contexts for a variety of diverse learners. You will be introduced to, and learn how to use state-of-the-art fabrication machines (3D printers, laser cutters, Go Go Boards, Sensors, etc.) to design educational toolkits, educational toys, science kits, and tangible user interfaces. The lab builds on the the theoretical and evidence-based foundations explored in the EDUC 236 / CS 402 Practicum. Interested students must also register for either EDUC 236 or CS 402, complete the application at http://bit.ly/educ236 by January 4 at 5 p.m., and come to the first class at 8:30 a.m. in CERAS 108.
Terms: Win | Units: 1-3 | Grading: Letter (ABCD/NP)
Instructors: Grant, W. (PI)

CS 421: Designing AI to Cultivate Human Well-Being

Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating more »
Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating human well-being. The goal of this class is to bridge the gap between technology and societal objectives: How do we design AI to promote human flourishing? On Day 1, we draw on behavioral research to discuss what makes humans thrive. Behavioral research shows that for people to flourish, they need meaning, which involves an ability to understand and value others, a sense of belonging, and knowledge that they are making a contribution bigger than themselves. The conditions for this occur when people feel they have the resources and insight to establish a sense of meaning for themselves. Students will draw on this research to focus on building AI technology that effectively understands, communicates with, collaborates with and augment people. On days 2-5, leaders across industries (e.g., healthcare, transportation) that fundamentally affect human wellbeing will participate in lightning round exchanges to delve deeply into the challenge of building technology focused on human well-being, followed by interactive discussion with students. On the last day, the four-person cross-disciplinary teams will present their 2 page white paper proposals to invited guests. Of note: this course is entirely about high-level "programming" and provides no technical insight on machine learning, data-mining or statistical pattern recognition.
Terms: Win | Units: 2 | Grading: Satisfactory/No Credit

CS 424M: Learning Analytics and Computational Modeling in Social Science (EDUC 390)

Computational modeling and data-mining are dramatically changing the physical sciences, and more recently also the social and behavioral sciences. Traditional analysis techniques are insufficient to investigate complex dynamic social phenomena as social networks, online gaming, diffusion of innovation, opinion dynamics, classroom behavior, and other complex adaptive systems. In this course, we will learn about how modeling, network theory, and basic data-mining can support research in cognitive, and social sciences, in particular around issues of learning, cognitive development, and educational policy.
Terms: not given this year, last offered Winter 2013 | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 428: Computation and Cognition: The Probabilistic Approach (PSYCH 204)

This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 431: High-level Vision: From Neurons to Deep Neural Networks (PSYCH 250)

Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, and Computer Science. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit

CS 448: Topics in Computer Graphics

Topic changes each quarter. Recent topics: computational photography, datanvisualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offererings and prerequisites. May be repeated for credit.
Terms: not given this year, last offered Autumn 2007 | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit
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