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Random matrix methods for machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Random matrix methods for machine learningRomain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology.
Author:	Couillet, Romain.
other author:	Liao, Zhenyu.
Published:	Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press,2022.
Description:	vi, 402 p. :ill., digital ;25 cm.
Notes:	Title from publisher's bibliographic system (viewed on 30 Jun 2022).
Subject:	Machine learningMathematics.
Online resource:	https://doi.org/10.1017/9781009128490
ISBN:	9781009128490$q(electronic bk.)

Random matrix methods for machine learning
Couillet, Romain.

Random matrix methods for machine learning[electronic resource] /Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology. - Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press,2022. - vi, 402 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 30 Jun 2022).

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.

ISBN: 9781009128490$q(electronic bk.)Subjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .C69 2022

Dewey Class. No.: 006.31

Random matrix methods for machine learning
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245 1 0 $a Random matrix methods for machine learning $h [electronic resource] / $c Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology.
260 $a Cambridge, United Kingdom ; New York, NY, USA : $b Cambridge University Press, $c 2022.
300 $a vi, 402 p. : $b ill., digital ; $c 25 cm.
500 $a Title from publisher's bibliographic system (viewed on 30 Jun 2022).
520 $a 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.
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856 4 0 $u https://doi.org/10.1017/9781009128490