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141 - 150 of 204 results for: CS

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

CS 232: Digital Image Processing (EE 368)

Image sampling and quantization color, point operations, segmentation, morphological image processing, linear image filtering and correlation, image transforms, eigenimages, multiresolution image processing, noise reduction and restoration, feature extraction and recognition tasks, image registration. Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab and optionally on Android mobile devices. Term project. In the fall and spring quarter, a sequence of interactive web/video modules substitutes the classroom lectures. In the winter quarter, the course is taught conventionally; both versions of the course are equivalent. Recommended: EE261, EE278B.
Terms: Aut, Win, Spr | Units: 3 | Grading: Letter (ABCD/NP)

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 | Grading: Letter or Credit/No Credit

CS 244: Advanced Topics in Networking

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: Katti, S. (PI)

CS 244C: Readings and Projects in Distributed Systems

Companion project option for 244B. Corequisite: 244B.
Terms: offered occasionally | Units: 3-6 | Grading: Letter or Credit/No Credit

CS 244E: Networked Wireless Systems

Design and implementation of wireless networks and mobile systems. The course will commence with a short retrospective of wireless communication and initially touch on some of the fundamental physical layer properties of various wireless communication technologies. The focus will then shift to design of media access control and routing layers for various wireless systems. The course will also examine adaptations necessary at transport and higher layers to cope with node mobility and error-prone nature of the wireless medium. Finally, it will conclude with a brief overview of other related issues including emerging wireless/mobile applications. Prerequisites: EE 284
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 246: Mining Massive Data Sets

Distributed file systems: Hadoop, map-reduce; PageRank, topic-sensitive PageRank, spam detection, hubs-and-authorities; similarity search; shingling, minhashing, random hyperplanes, locality-sensitive hashing; analysis of social-network graphs; association rules; dimensionality reduction: UV, SVD, and CUR decompositions; algorithms for very-large-scale mining: clustering, nearest-neighbor search, gradient descent, support-vector machines, classification, and regression; submodular function optimization. Prerequisites: At lease one of CS107 or CS145; at least one of CS109 or STAT116, or equivalent.
Terms: Win | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Leskovec, J. (PI)

CS 246H: Mining Massive Data Sets Hadoop Lab

Supplement to CS 246 providing additional material on Hadoop. Students will learn how to implement data mining algorithms using Hadoop, how to implement and debug complex MapReduce jobs in Hadoop, and how to use some of the tools in the Hadoop ecosystem for data mining and machine learning. Topics: Hadoop, MapReduce, HDFS, combiners, secondary sort, distributed cache, SQL on Hadoop, Hive, Cloudera ML/Oryx, Mahout, Hadoop streaming, implementing Hadoop jobs, debugging Hadoop jobs, TF-IDF, Pig, Sqoop, Oozie, HBase, Impala. Prerequisite: CS 107 or equivalent.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit

CS 247: Human-Computer Interaction Design Studio

Project-based focus on interaction design process, especially early-stage design and rapid prototyping. Methods used in interaction design including needs analysis, user observation, sketching, concept generation, scenario building, and evaluation. Prerequisites: 147 and 106A or equivalent background in programming.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)

CS 249B: Large-scale Software Development

Software engineering of high quality large-scale complex software with a focus on evolvability, performance and cost. Software development processes, people and practice; audit: integrating invariant checks with production software; concurrency with modular object-oriented programming; collection implementation; generic programming and templates; design of value types; named descriptions for large value types; memory management; controlling placement, locality and consumption; run-time vs. static type checking and identification.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit
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