Random Matrix Methods for Machine Learning
Book Details
Format
Hardback or Cased Book
ISBN-10
1009123238
ISBN-13
9781009123235
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jul 21st, 2022
Print length
408 Pages
Weight
876 grams
Dimensions
17.70 x 25.10 x 2.80 cms
Product Classification:
Probability & statisticsData capture & analysisMachine learningSignal processing
Ksh 12,550.00
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For graduate students, practitioners, and sophisticated users, this book offers a tutorial approach to the foundations of random matrix theory for machine learning and systematic analyses of advanced applications ranging from power detection to deep neural networks. MATLAB and Python code is provided for all concepts and applications.
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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