3 Optimization

Chapters in this book

1. Frontmatter I
2. Preface V
3. Contents VII
4. Part I: Introduction and preliminaries
5. 1 Introduction to machine learning 1
6. 2 Probability review 14
7. 3 Optimization 34
8. Part II: Supervised learning
9. 4 Statistical learning theory 57
10. 5 Linear models 71
11. 6 Kernel methods 88
12. 7 Gaussian processes 103
13. 8 Deep learning 117
14. 9 Ensemble methods 147
15. Part III: Beyond supervised learning
16. 10 Topics in unsupervised learning 163
17. 11 Reinforcement learning 177
18. Bibliography 193
19. Index 197