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CS 399: Independent Project

Letter grade only.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for 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) ; Borenstein, J. (PI) ; Boyd, S. (PI) ; Bradski, G. (PI) ; Brafman, R. (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) ; Goel, S. (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) ; 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) ; MacCartney, B. (PI) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (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) ; 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) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (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) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 399P: Independent Project

Graded satisfactory/no credit.
Terms: Aut, Win, Spr, Sum | Units: 1-9 | Repeatable for 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) ; 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) ; 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) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (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) ; 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) ; 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)

Practicum in designing and building technology-enabled curricula and hands-on learning environments. Students use software toolkits and state-of-the-art fabrication machines to design educational software, educational toolkits, and tangible user interfaces. The course will focus on designing low-cost technologies, particularly for urban school in the US and abroad. We will explore theoretical and design frameworks from the constructionist learning perspective, critical pedagogy, interaction design for children.
Terms: Win | Units: 3-4
Instructors: Blikstein, P. (PI) ; Schneider, B. (PI)

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

This course is a hands-on lab in the prototyping and fabrication of tangible technologies, with a special focus in learning and education. We will learn how to use state-of-the-art fabrication machines (3D printers, 3D scanners, laser cutters, routers) to design educational toolkits, educational toys, science kits, and tangible user interfaces. A special focus of the course will be to design low-cost technologies, particularly for urban school in the US and abroad.
Terms: Win | Units: 1-3
Instructors: Blikstein, P. (PI) ; Bumbacher, E. (PI)

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.
Last offered: Winter 2013

CS 424P: Extracting Social Meaning and Sentiment (LINGUIST 287)

Methods for extracting social meaning (speaker perspectives, emotions and attitudes) from text and speech. Topics include sentiment analysis and summarization, detection of deception, sarcasm, emotion, and personality.nnAnalysis of meaning-bearing characteristics of the speaker and topic, including text, discourse, prosodic and other cues. Prerequisite: CS 124 or 221 or 229 or permission of instructors.
Last offered: Autumn 2010

CS 431: High-Level Vision: Object Representation (PSYCH 250)

(Formerly CS423 High-Level Vision: Behaviors, Neurons, and Computational Models) 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, Computer Science and Applied Statistics. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field. Topics include: theories of object perception, neural computations underlying invariant object perception, how visual exemplars and categories are represented in the brain, what information is present in distributed activations across neural populations and how it relates to object perception, what modern statistical and analytical tools there are for multi-variate analysis of brain activations.
Last offered: Spring 2014

CS 448: Topics in Computer Graphics

Topic changes each quarter. Recent topics: computational photography, datannvisualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offererings and prerequisites. May be repeated for credit.
Last offered: Autumn 2007 | Repeatable for credit

CS 448B: Data Visualization

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of 147, 148, or equivalent.
Terms: Spr | Units: 3 | Repeatable for credit
Instructors: Agrawala, M. (PI) ; Cheng, Y. (TA) ; Schubert, L. (TA) ; Washington, P. (TA)

CS 448H: Topics in Computer Graphics: Domain-Specific Languages for Graphics, Imaging, and Beyond

Topic changes each quarter. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offerings and prerequisites. May be repeated for credit.
Terms: Aut | Units: 3
Instructors: Hanrahan, P. (PI) ; Bernstein, G. (TA)
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