Academics / Courses / DescriptionsCOMP_SCI 496: Theoretical Foundations of Data Science
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Prerequisites
CS 212 or equivalents (in mathematics and statistics). CS 336 is also highly recommended (particularly for CS majors).Description
Modern datasets across a variety of application domains in machine learning, genomics, social network analysis etc. are massive and high-dimensional. How can we design algorithms that allow us to process and analyze these high-dimensional data efficiently?
This course will provide an introduction to the mathematical and algorithmic foundations of data science. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, algorithms for clustering, probabilistic modeling, factor analysis and other machine learning problems.
REQUIRED TEXTBOOK: This course will use the following book by Blum, Hopcroft and Kannan ( https://www.amazon.com/Foundations-Data-Science-Avrim-Blum/dp/1108485065 ) that can also be accessed online for free (https://ttic.uchicago.edu/~avrim/book.pdf ).
INSTRUCTOR: Prof. Vijayaraghavan