Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, Shai Ben-David.

By:

Shalev-Shwartz, Shai [author]

Contributor(s):

Ben-David, Shai [author]

Material type:

TextPublisher: New York : Cambridge University Press, 2014Description: xvi, 397 pages : illustrations ; 26 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781107057135
1107057132 (hardback)

Subject(s):

DDC classification:

006.31 23

Contents:

1. Introduction -- Part 1. Foundations -- 2. A gentle start -- 3. A formal learning model -- 4. Learning via uniform convergence -- 5. The bias-complexity tradeoff -- 6. The VC-dimension -- 7. Non-uniform learnability -- 8. The runtime of learning -- Part 2. 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 3. Additional Learning Models -- 21. Online learning -- 22. Clustering -- 23. Dimensionality reduction -- 24. Generative models -- 25. Feature selection and generation -- Part 5. 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.

Summary: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

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Includes bibliographical references (pages 385-393) and index.

1. Introduction --
Part 1. Foundations --
2. A gentle start --
3. A formal learning model --
4. Learning via uniform convergence --
5. The bias-complexity tradeoff --
6. The VC-dimension --
7. Non-uniform learnability --
8. The runtime of learning --
Part 2. 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 3. Additional Learning Models --
21. Online learning --
22. Clustering --
23. Dimensionality reduction --
24. Generative models --
25. Feature selection and generation --
Part 5. 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.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

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