121 - 130 of 206 results for: CS

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.

(Formerly CS323) The field of computer vision has seen an explosive growth in past decade. Much of recent effort in vision research is towards developing algorithms that can perform high-level visual recognization tasks on real-world images and videos. With development of Internet, this task becomes particularly challenging and interesting given the heterogeneous data on the web. Course will focus on reading recent research papers that are focused on solving high-level visual recognition problems, such as object recognition and categorization, scene understanding, human motion understanding, etc. Project required. Prerequisite: some experience in research with one of the following fields: computer vision, image processing, computer graphics, machine learning.

The course surveys recent developments in high level and 3D computer vision and will focus on reading recent research papers on topics related to 3D object recognition and representation, spatial inference, activity understanding, human vision and 3D perception. The course is inspired by a famous series of workshops (called 3d-RR) which have been offered during the International Conference in Computer Vision (ICCV) since 2007. Prerequisites - Some experience in research with one of the following fields: computer vision, image processing, computer graphics, machine learning.

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.

Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.

Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution; some lectures on relevant materials will be given: Hadoop, Hive, Amazon EC2; other topics of possible relevance to some projects: computational advertising and the adwords problem; graph partitioning and community detection; extracting relations from the Web; stream data processing.

Topics change every year. May be repeated for credit. Prerequisite: 243.

The first part of the course will describe classical database systems topics, including join processing, concurrency control, recovery, query optimization, and database theory. On each topic, there will be an in-depth discussion of a few representative papers and recent results. The second part of the course will focus on additional topics that are relevant to database systems, including MapReduce-style processing, information extraction, and predictive analytics. The course readings will primarily consist of classical and recent research papers. Prerequisites: CS 145 or equivalent.

A major database system implementation project realizes the principles and techniques covered in earlier courses. Students independently build a complete database management system, from file structures through query processing, with a personally designed feature or extension. Lectures on project details and advanced techniques in database system implementation, focusing on query processing and optimization. Guest speakers from industry on commercial DBMS implementation techniques. Prerequisites: 145, 245, programming experience in C++.