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Algorithms for Big Data: Home Page
Instructor: Chandra Chekuri
Department: Computer Science
Institution: University of Illinois Urbana Champaign
Platform: Independent
Year: 2014
Price: Free
Prerequisites: algorithms, discrete mathematic, probability, linear algebra

This is a graduate level class and a reasonable background in algorithms and discrete mathematics would be needed. Knowledge and exposure to probability and linear algebra is necessary.

This course will describe some algorithmic techniques developed for handling large amounts of data that is often available in limited ways. Topics that will be covered include data stream algorithms, sampling and sketching techniques, and sparsification, with applications to signals, matrices, and graphs. Emphasis will be on the theoretical aspects of the design and analysis of such algorithms.