CS 498 ABG - Special Topics - Algorithms for Big Data
Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.
Entirely online with some mix of asynchronous and synchronous components that may vary over the semester and based on needs of students and instructor. This course will describe some algorithmic techniques that have been developed for handling large amounts of data which may not fit in memory or is available in limited ways. Topics include data stream algorithms, sampling and sketching techniques, sparsification methods, and parallelization with applications to signals, matrices, and graphs. Emphasis will be on theoretical aspects of the design and analysis of such algorithms. Strongly suggested Prerequisites: grades of at least B+ in CS 374 and CS 361, or comparable understanding and facility with algorithms and probability. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister
Option 1Number of Required Visit(s): 0
Course Level: Graduate