Random Matrix Methods for Machine Learning

Reviews / Votes

'Roman Couillet's book is unique among books on random matrix theory in that it provides a solid yet accessible introduction to the theory and its transformative potential in applications. After presenting a coherent and uncluttered introduction of the theory, several chapters illustrate how it applies to important problems, including, high dimensional statistical inference, neural networks, random graphs, and convex optimization. Written in a self-contained and exceptionally clear style this book will be of great utility to researchers in machine learning, statistics and signal processing who want to learn about how random matrix theory can be applied.' Alfred Hero, University of Michigan 'This book is a reference for all engineers and researchers interested in the recent mathematical advances in Machine Learning. It's a real 'tour de force' that fruitfully combines the mathematical elegance of random matrix theory methods with an impressive range of real-world applications.' Merouane Debbah, Huawei France Research Center 'This is a very timely and important book. Romain Couillet and Zhenyu Liao provide a great entry point into active, recent research on the applications of Random Matrix Theory as it pertains to high-dimensional statistics and analysis of machine learning algorithms. RMT was born in statistics with Wishart and later became, via Wigner, a great pillar of quantum and statistical before being recently pushed by mathematicians to deeper universality results. It is quite fitting that it now comes back to the modern problems and methods of statistics with this very well-organized and carefully written book by two leading experts.' Gerard Ben Arous, Courant Institute of Mathematical Sciences, New York University