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CS 199P: Independent Work

(Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Agrawala, M. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Angst, R. (PI) ; Baker, M. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (PI) ; Borenstein, J. (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) ; 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) ; 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) ; Lin, H. (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) ; Parlante, N. (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) ; Saxena, A. (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) ; Williams, V. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 210A: Software Project Experience with Corporate Partners

Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 109 and 110.
Terms: Win | Units: 3-4
Instructors: Borenstein, J. (PI) ; Huang, M. (TA) ; Liu, D. (TA) ; Sebastian, E. (TA)

CS 223A: Introduction to Robotics (ME 320)

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win | Units: 3
Instructors: Khatib, O. (PI) ; Ganguly, S. (TA) ; Kazi, S. (TA)

CS 228: Probabilistic Graphical Models: Principles and Techniques

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Win | Units: 3-4

CS 229T: Statistical Learning Theory (STATS 231)

(Same as STATS 231) How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include online learning, kernel methods, generalization bounds (uniform convergence), and spectral methods. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning ( STATS 315A or CS 229). Convex optimization ( EE 364a) is helpful but not required.
Terms: Win | Units: 3
Instructors: Liang, P. (PI) ; Bastani, O. (TA) ; Xu, P. (TA)

CS 231N: Convolutional Neural Networks for Visual Recognition

Computer Vision has become ubiquitous in our society, with applications innsearch, image understanding, apps, mapping, medicine, drones, andnself-driving cars. Core to many of these applications are the tasks of image classification, localization and detection. This course is a deep dive into details of neural network architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge: http://image-net.org/challenges/LSVRC/2014/index. Prerequisites: Proficiency in Python; familiarity with C/C++; CS 131 and CS 229 or equivalents; Math 21 or equivalent, linear algebra.
Terms: Win | Units: 3-4

CS 239: Advanced Topics in Sequential Decision Making (AA 229)

Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites: AA 228/ CS 238 or CS 221.
Terms: Win | Units: 3-4
Instructors: Kochenderfer, M. (PI) ; Cox, J. (TA)

CS 240H: Functional Systems in Haskell

Covers an array of practical issues and techniques that arise when building real-world systems in the Haskell programming language. Topics include the basics of Haskell, laziness, monads, parsers, testing and debugging, performance tuning, interfacing to native code, concurrency and I/O paradigms, language extensions, meta-programming, and applications to the web and security. Concepts will be reinforced through a few individual programming assignments followed by a larger team project. Prior familiarity with Haskell may be helpful but is not required. Prerequisites: CS106B or 106X.
Terms: Win | Units: 3-4
Instructors: Mazieres, D. (PI) ; Terei, D. (TA) ; Wahby, R. (TA)

CS 243: Program Analysis and Optimizations

Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection. Prerequisites: 103 or 103B, and 107.
Terms: Win | Units: 3-4
Instructors: Lam, M. (PI) ; Dodhia, N. (TA) ; Ramesh, R. (TA)

CS 245: Database Systems Principles

File organization and access, buffer management, performance analysis, and storage management. Database system architecture, query optimization, transaction management, recovery, concurrency control. Reliability, protection, and integrity. Design and management issues. Prerequisites: 145, 161.
Terms: Win | Units: 3
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