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61 - 70 of 204 results for: CS

CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288)

Machine understanding of human language. Computational semantics (determination of word sense and synonymy, event structure and thematic roles, time, aspect, causation, compositional semantics, scopal operators), and computational pragmatics and discourse (coherence, coreference resolution, information packaging, dialogue structure). Theoretical issues, online resources, and relevance to applications including question answering and summarization. Prerequisites: one of LINGUIST 180 / CS 124 / CS 224N,S: and logic such as LINGUIST 130A or B, CS 157, or PHIL150).
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 224W: Social and Information Network Analysis

(Formerly 322) How do rumors and information spread? Who are the influencers? Can we predict friendships on Facebook? Networks are the core of the WWW, blogs, Twitter and Facebook. They can be characterized by the complex interplay between information content, millions of individuals and organizations that create it, and the technology that supports it. Course will focus on how to analyze the structure and dynamics of large networks, how to model links, and how design algorithms that work with such large networks. Topics: statistical properties of large networks, models of social network structure and evolution, link prediction, network community detection, diffusion of innovation, information propagation, six-degrees of separation, finding influential nodes in networks, disease outbreak detection, networks with positive and negative ties, and connections with work in the social sciences and economics.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI)

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: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 227B: General Game Playing

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

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 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

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 240E: Embedded Wireless Systems

The structure and implementation of hardware/software systems for low power embedded sensors; how to build hardware/software systems that can run unattended for years on small batteries. Topics: hardware trends, energy profiles, execution models, sensing, aggregation, storage, application requirements, allocation, power management, resource management, scheduling, time synchronization, programming models, software design, and fault tolerance. Students discuss papers and research a final project building working systems on low-power embedded devices.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Levis, P. (PI)
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