Random Matrix Methods For Machine Learning at Meripustak

Random Matrix Methods For Machine Learning

Books from same Author: Romain Couillet And Zhenyu Liao

Books from same Publisher: Cambridge University Press

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  • General Information  
    Author(s)Romain Couillet And Zhenyu Liao
    PublisherCambridge University Press
    ISBN9781009123235
    Pages408
    BindingHardcover
    LanguageEnglish
    Publish YearJanuary 2022

    Description

    Cambridge University Press Random Matrix Methods For Machine Learning by Romain Couillet And Zhenyu Liao

    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.