Table of Contents:

Part I: Introduction
1. Foundations
2. Probability: Univariate Models
3. Probability: Multivariate Models
4. Statistics
5. Decision Theory
6. Information Theory
7. Linear Algebra
8. Optimization

Part II: Linear Models
9.Linear Discriminant Analysis
10. Logistic Regression
11. Linear Regression
12. Generalized Linear Models

Part III: Deep Neural Networks
13. Neural Networks for Structured Data
14 .Neural Networks for Images
15. Neural Networks for Sequences

Part IV: Nonparametric Models
16. Exemplar-based Methods
17. Kernel Methods
18. Trees, Forests, Bagging, and Boosting

Part V: Beyond Supervised Learning
19. Learning with Fewer Labeled Examples
20. Dimensionality Reduction
21. Clustering
22. Recommender Systems
23. Graph Embeddings

This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR.