Mathematical Analysis of Machine Learning Algorithms

ISBN-13: 9781009098380 / Twarda / 2023 / 479 str.

Tong (Hong Kong University of Science and Technology) Zhang

Opis

Introduction to the mathematical foundation for understanding and analyzing machine learning algorithms for AI students and researchers.

Recenzje

'This book gives a systematic treatment of the modern mathematical techniques that are commonly used in the design and analysis of machine learning algorithms. Written by a key contributor to the field, it is a unique resource for graduate students and researchers seeking to gain a deep understanding of the theory of machine learning.' Shai Shalev-Shwartz, Hebrew University of Jerusalem

Spis treści

1. Introduction; 2. Basic probability inequalities for sums of independent random variables; 3. Uniform convergence and generalization analysis; 4. Empirical covering number analysis and symmetrization; 5. Covering number estimates; 6. Rademacher complexity and concentration inequalities; 7. Algorithmic stability analysis; 8. Model selection; 9. Analysis of kernel methods; 10. Additive and sparse models; 11. Analysis of neural networks; 12. Lower bounds and minimax analysis; 13. Probability inequalities for sequential random variables; 14. Basic concepts of online learning; 15. Online aggregation and second order algorithms; 16. Multi-armed bandits; 17. Contextual bandits; 18. Reinforcement learning; A. Basics of convex analysis; B. f-Divergence of probability measures; References; Author index; Subject index.