Understanding Machine Learning From Theory to Algorithms 1st Edition Shwartz Solutions Manual
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Understanding Machine Learning From Theory to Algorithms 1st Edition Shwartz Solutions Manual.
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Understanding Machine Learning From Theory to Algorithms 1st Edition Shwartz Solutions Manual
Product details:
- ISBN-10 : 1107057132
- ISBN-13 : 978-1107057135
- Author: Shai Shalev-Shwartz, Shai Ben-David
Table 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
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