Winner of the 2019 PROSE Award for Mathematics, from the Association of American Publishers | Book - Cambridge University Press

Be sure to check out the New edition of High-Dimensional Probability, An Introduction with Applications in Data Science by Roman Vershynin, University of California, Irvine. 

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability...

A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.

• Closes the gap between the standard probability curriculum and what mathematical data scientists need to know
• Selects the core ideas and methods and presents them systematically with modern motivating applications to bring readers quickly up to speed
• Features integrated exercises that invite readers to sharpen their skills and build practical intuition

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Source: Cambridge University Press