This page contains the
prepublication version and slides that are derived from the book Mathematical Analysis of Machine
Learning Algorithms
(© 2021-2023 Tong
Zhang). The prepublication version of the book is free to view and download for personal
use only. Not for redistribution or commercial use. The slides can
be used for non-commercial purposes.

You may cite the book as follows:

@book{zhang_2023_ltbook, title={Mathematical Analysis of Machine Learning Algorithms}, author={Zhang, Tong}, doi={10.1017/9781009093057}, publisher={Cambridge University Press}, place={Cambridge}, year={2023} }

The following slides contain simplified versions of the book's results, making them more easily comprehensible. Examining these slides could be helpful.

- Chapter 1: Introduction
- Chapter 2: Basic Probability Inequalities
- Chapter 3: Uniform Convergence
- Chapter 4: Empirical Covering Number Analysis and Symmetrization
- Chapter 5: Covering Number Estimates
- Chapter 6: Rademacher Complexity and Concentration Inequalities
- Chapter 7: Algorithmic Stability Analysis
- Chapter 8: Model Selection
- Chapter 9: Analysis of Kernel Methods
- Chapter 10: Additive and Sparse Models
- Chapter 11: Analysis of Neural Networks
- Chapter 12: Lower Bounds and Minimax Analysis
- Chapter 13: Probability Inequalities for Sequential Random Variables
- Chapter 14: Basic Concepts of Online Learning
- Chapter 15: Online Aggregation and Second Order Algorithms
- Chapter 16: Multi-armed Bandits
- Chapter 17: Contextual Bandits
- Chapter 18: Reinforcement Learning