Understanding Machine Learning: From Theory To Algorithms

Table of Contents

1. Introduction
Part I. Foundations:
2. A gentle start
3. A formal learning model
4. Learning via uniform convergence
5. The bias-complexity trade-off
6. The VC-dimension
7. Non-uniform learnability
8. The runtime of learning
Part II. From Theory to Algorithms:
9. Linear predictors
10. Boosting
11. Model selection and validation
12. Convex learning problems
13. Regularization and stability
14. Stochastic gradient descent
15. Support vector machines
16. Kernel methods
17. Multiclass, ranking, and complex prediction problems
18. Decision trees
19. Nearest neighbor
20. Neural networks
Part III. Additional Learning Models:
21. Online learning
22. Clustering
23. Dimensionality reduction
24. Generative models
25. Feature selection and generation
Part IV. Advanced Theory:
26. Rademacher complexities
27. Covering numbers
28. Proof of the fundamental theorem of learning theory
29. Multiclass learnability
30. Compression bounds
31. PAC-Bayes
Appendix A. Technical lemmas
Appendix B. Measure concentration
Appendix C. Linear algebra.