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61 - 70 of 215 results for: CS

CS 225A: Experimental Robotics

Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. Prerequisites: 223A or equivalent.
Terms: Aut, Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Khatib, O. (PI)

CS 229: Machine Learning

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Ng, A. (PI)

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 | Grading: Letter or Credit/No Credit
Instructors: Liang, P. (PI)

CS 231A: Computer Vision: From 3D Reconstruction to Recognition

(Formerly 223B) An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Prerequisites: linear algebra, basic probability and statistics.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Savarese, S. (PI)

CS 231B: The Cutting Edge of Computer Vision

(Formerly 223C) More than one-third of the brain is engaged in visual processing, the most sophisticated human sensory system. Yet visual recognition technology has fundamentally influenced our lives on the same scale and scope as text-based technology has, thanks to Google, Twitter, Facebook, etc. This course is designed for those students who are interested in cutting edge computer vision research, and/or are aspiring to be an entrepreneur using vision technology. Course will guide students through the design and implementation of three core vision technologies: segmentation, detection and classification on three highly practical, real-world problems. Course will focus on teaching the fundamental theory, detailed algorithms, practical engineering insights, and guide them to develop state-of-the-art systems evaluated based on the most modern and standard benchmark datasets. Prerequisites: CS2223B or equivalent and a good machine learning background (i.e. CS221, CS228, CS229). Fluency in Matlab and C/C++.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Li, F. (PI)

CS 231M: Mobile Computer Vision

The course surveys recent developments in computer vision, graphics and image processing for mobile application. Topics of interest include: feature extraction, image enhancement and digital photography, 3D scene understanding and modeling, virtual augmentation, object recognition and categorization, human activity recognition. As part of this course, students will familiarize with a state-of-the-art mobile hardware and software development platform: an NVIDIA Tegra-based Android tablet, with relevant libraries such as OpenCV and FCam. Tablets will be available for each student team. Prerequisites: Knowledge of linear algebra, probability, as well as concepts introduced in either CS131A or CS231A and CS232 (or equivalent) are necessary for understanding the material covered in this class. C++ (or Java) programming experience is expected.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Savarese, S. (PI)

CS 240: Advanced Topics in Operating Systems

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.
Terms: Spr | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: Engler, D. (PI)

CS 242: Programming Languages

Central concepts in modern programming languages, impact on software development, language design trade-offs, and implementation considerations. Functional, imperative, and object-oriented paradigms. Formal semantic methods and program analysis. Modern type systems, higher order functions and closures, exceptions and continuations. Modularity, object-oriented languages, and concurrency. Runtime support for language features, interoperability, and security issues. Prerequisite: 107, or experience with Lisp, C, and an object-oriented language.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 244: Advanced Topics in Networking (EE 284B)

Classic papers, new ideas, and research papers in networking. Architectural principles: naming, addressing, routing; congestion control, traffic management, QoS; wireless and mobility; overlay networks and virtualization; network security; switching and routing; content distribution; and proposals for future Internet structures. Prerequisite: 144 or equivalent.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: McKeown, N. (PI)

CS 244B: Distributed Systems

Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites: CS 144 and CS 249A.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
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