Understanding Machine Learning: From Theory to Algorithms > 인공지능

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.