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61 - 70 of 89 results for: CS ; Currently searching autumn courses. You can expand your search to include all quarters

CS 331B: Representation Learning in Computer Vision

A representation performs the task of converting an observation in the real world (e.g. an image, a recorded speech signal, a word in a sentence) into a mathematical form (e.g. a vector). This mathematical form is then used by subsequent steps (e.g. a classifier) to produce the outcome, such as classifying an image or recognizing a spoken word. Forming the proper representation for a task is an essential problem in modern AI. In this course, we focus on 1) establishing why representations matter, 2) classical and moderns methods of forming representations in Computer Vision, 3) methods of analyzing and probing representations, 4) portraying the future landscape of representations with generic and comprehensive AI/vision systems over the horizon, and finally 5) going beyond computer vision by talking about non-visual representations, such as the ones used in NLP or neuroscience. The course will heavily feature systems based on deep learning and convolutional neural networks. We will hav more »
A representation performs the task of converting an observation in the real world (e.g. an image, a recorded speech signal, a word in a sentence) into a mathematical form (e.g. a vector). This mathematical form is then used by subsequent steps (e.g. a classifier) to produce the outcome, such as classifying an image or recognizing a spoken word. Forming the proper representation for a task is an essential problem in modern AI. In this course, we focus on 1) establishing why representations matter, 2) classical and moderns methods of forming representations in Computer Vision, 3) methods of analyzing and probing representations, 4) portraying the future landscape of representations with generic and comprehensive AI/vision systems over the horizon, and finally 5) going beyond computer vision by talking about non-visual representations, such as the ones used in NLP or neuroscience. The course will heavily feature systems based on deep learning and convolutional neural networks. We will have several teaching lectures, a number of prominent external guest speakers, as well as presentations by the students on recent papers and their projects. nnRequired Prerequisites: CS131A, CS231A, CS231B, or CS231N. If you do not have the required prerequisites, please contact a member of the course staff before enrolling in this course.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 332: Advanced Survey of Reinforcement Learning

This class will provide a core overview of essential topics and newnresearch frontiers in reinforcement learning. Planned topics include:nmodel free and model based reinforcement learning, policy search, MontenCarlo Tree Search planning methods, off policy evaluation, exploration,nimitation learning, temporal abstraction/hierarchical approaches, safetynand risk sensitivity, human-in-the-loop RL, inverse reinforcementnlearning, learning to communicate, and insights from human learning.nStudents are expected to create an original research paper on a relatedntopic. Prerequisites: CS221 or AA238/CS238 or CS234 or CS229 or similarnexperience.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 333: Safe and Interactive Robotics

Once confined to the manufacturing floor, robots are quickly entering the public space at multiple levels: drones, surgical robots, service robots, and self-driving cars are becoming tangible technologies impacting the human experience. Our goal in this class is to learn about and design algorithms that enable robots to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on.nThis is a project-based graduate course that studies algorithms in formal methods, control theory, and robotics, which can improve the state-of-the-art human-robot systems. We focus on designing new algorithms for enhancing safe and interactive autonomy. nnRecommended: Introductory course in AI and robotics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Sadigh, D. (PI)

CS 348C: Computer Graphics: Animation and Simulation

Core mathematics and methods for computer animation and motion simulation. Traditional animation techniques. Physics-based simulation methods for modeling shape and motion: particle systems, constraints, rigid bodies, deformable models, collisions and contact, fluids, and fracture. Animating natural phenomena. Methods for animating virtual characters and crowds. Additional topics selected from data-driven animation methods, realism and perception, animation systems, motion control, real-time and interactive methods, and multi-sensory feedback. Recommended: CS 148 and/or 205A. Prerequisite: linear algebra.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: James, D. (PI)

CS 349D: Cloud Computing Technology

The largest change in the computer industry over the past five years has arguably been the emergence of cloud computing: organizations are increasingly moving their workloads to managed public clouds and using new, global-scale services that were simply not possible in private datacenters. However, both building and using cloud systems remains a black art with many difficult research challenges. This research seminar will cover industry and academic work on cloud computing and survey challenges including programming interfaces, cloud native applications, resource management, pricing, availability and reliability, privacy and security. Students will also propose and develop an original research project.n nPrerequisites: For graduate students, background in computer systems ( CS 240, 244, 244B or 245) is strongly recommended. Undergrads will need instructor's approval.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 375: Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249)

Introduction to designing, building, and training neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics, memory and attention; integration of variational and generative methods for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models, and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognitive or systems neuroscience.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Yamins, D. (PI)

CS 376: Human-Computer Interaction Research

Prepares students to conduct original HCI research by reading and discussing seminal and cutting-edge research papers. Main topics are ubiquitous computing, social computing, and design and creation; breadth topics include HCI methods, programming, visualization, and user modeling. Student pairs perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students, an A- or better in CS 147 or CS 247. No prerequisite for PhD students or students outside of CS and Symbolic Systems.
Terms: Aut | Units: 3-4 | Repeatable for credit | Grading: Letter (ABCD/NP)

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 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. 390 A, B, and C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: 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) ; 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) ; Dwork, C. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fischer, M. (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) ; 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) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (PI) ; Mitra, 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) ; 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) ; 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) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Williams, V. (PI) ; Winograd, T. (PI) ; Winstein, K. (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 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. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | 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) ; 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) ; 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) ; 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) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (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) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (PI) ; Mitra, S. (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) ; 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) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, 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 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. 390A,B,C may each be taken once.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Aiken, A. (PI) ; Akeley, K. (PI) ; Altman, R. (PI) ; Baker, M. (PI) ; Barbagli, F. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (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) ; 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) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (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) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (PI) ; Mitra, S. (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) ; 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) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Schwarz, K. (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) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI)
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