Random Matrix Methods for Machine Learning (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	July 2022
Authors:	Romain Couillet • Zhenyu Liao
Dimensions:	251 x 174 x 23mm (L x W x T)
Format:	Hardcover
Pages:	408
ISBN-13:	978-1-00-912323-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Signal processing Books > Computing & IT > Applications of computing > Databases > Data capture & analysis Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-00-912323-8
Barcode:	9781009123235

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