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Full Description
Data science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer science, as well as many other areas. This book gives an introduction to the mathematical methods that form the foundations of machine learning and data science, presented by leading experts in computer science, statistics, and applied mathematics. Although the chapters can be read independently, they are designed to be read together as they lay out algorithmic, statistical, and numerical approaches in diverse but complementary ways.
This book can be used both as a text for advanced undergraduate and beginning graduate courses, and as a survey for researchers interested in understanding how applied mathematics broadly defined is being used in data science. It will appeal to anyone interested in the interdisciplinary foundations of machine learning and data science.
Contents
P. Drineas and M. W. Mahoney, Lectures on randomized numerical linear algebra
S. J. Wright, Optimization algorithms for data analysis
J. C. Duchi, Introductory lectures on stochastic optimization
P.-G. Martinsson, Randomized methods for matrix computations
R. Vershynin, Four lectures on probabilistic methods for data science
R. Ghrist, Homological algebra and data.
