Romain Couillet, Zhenyu Liao

Random Matrix Methods for Machine Learning

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 408
• Erscheinungstermin: 13. Oktober 2022
• Englisch
• Abmessung: 247mm x 173mm x 24mm
• Gewicht: 876g
• ISBN-13: 9781009123235
• ISBN-10: 1009123238
• Artikelnr.: 63978690

Autorenporträt

Romain Couillet is a Full Professor at Grenoble-Alpes University, France. Prior to that, he was a Full Professor at CentraleSupélec, University of Paris-Saclay. His research topics are in random matrix theory applied to statistics, machine learning, and signal processing. He is the recipient of the 2021 IEEE/SEE Glavieux prize, of the 2013 CNRS Bronze Medal, and of the 2013 IEEE ComSoc Outstanding Young Researcher Award.

Inhaltsangabe

Preface
1. Introduction
2. Random matrix theory
3. Statistical inference in Linear Models
4. Kernel methods
5. Large neural networks
6. Large dimensional convex optimization
7. Community detection on graphs
8. Universality and real data
Bibliography
Index.