Mathematical Analysis of Machine Learning Algorithms (Hardcover)

Tong Zhang

ISBN 10: 1009098381 / ISBN 13: 9781009098380

Published by Cambridge University Press, Cambridge, 2023

About this Item

Hardcover. The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning. This self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781009098380

Bibliographic Details

• Title: Mathematical Analysis of Machine Learning Algorithms (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2023
• Language: English
• ISBN 10: 1009098381
• ISBN 13: 9781009098380
• Binding: Hardcover
• Condition: new

About this title

Synopsis

The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications. Topics covered include the analysis of supervised learning algorithms in the iid setting, the analysis of neural networks (e.g. neural tangent kernel and mean-field analysis), and the analysis of machine learning algorithms in the sequential decision setting (e.g. online learning, bandit problems, and reinforcement learning). Students will learn the basic mathematical tools used in the theoretical analysis of these machine learning problems and how to apply them to the analysis of various concrete algorithms. This textbook is perfect for readers who have some background knowledge of basic machine learning methods, but want to gain sufficient technical knowledge to understand research papers in theoretical machine learning.

About the Author

Tong Zhang is Chair Professor of Computer Science and Mathematics at the Hong Kong University of Science and Technology, where his research focuses on machine learning, big data, and their applications. A Fellow of the IEEE, the American Statistical Association, and the Institute of Mathematical Statistics, Zhang has served as Chair or Area chair at major machine learning conferences such as NeurIPS, ICML, and COLT, and he has been an associate editor for several top machine learning publications including PAMI, JMLR, and 'Machine Learning.'