Author : Romain Couillet
Publisher : Cambridge University Press
ISBN 13 : 1009301896
Total Pages : 412 pages
Book Rating : 4.0/5 (93 download)
Book Synopsis Random Matrix Methods for Machine Learning by : Romain Couillet
Download or read book Random Matrix Methods for Machine Learning written by Romain Couillet and published by Cambridge University Press. This book was released on 2022-07-21 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.