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Understanding machine learning : from theory to algorithms
La quatrième de couverture indique : "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 a theoretica...
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| Auteurs principaux : | , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz,... Shai Ben-David,... |
| Publié : |
New York :
Cambridge University Press
, C 2014 |
| Description matérielle : | 1 vol. (XVI-397 p.) |
| Sujets : | |
| Documents associés : | Autre format:
Understanding machine learning |
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| 200 | 1 | |a Understanding machine learning |e from theory to algorithms |f Shai Shalev-Shwartz,... Shai Ben-David,... | |
| 214 | 0 | |a New York |c Cambridge University Press | |
| 214 | 4 | |d C 2014 | |
| 215 | |a 1 vol. (XVI-397 p.) |c ill., couv. ill. en coul. |d 26 cm | ||
| 305 | |a Autre tirage: 2018 | ||
| 320 | |a Bibliogr. p. 385-393. Index | ||
| 330 | |a La quatrième de couverture indique : "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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering." | ||
| 359 | 2 | |c 1. Introduction; |b Part I. Foundations: |c 2. A gentle start; |c 3. A formal learning model; |c 4. Learning via uniform convergence; |c 5. The bias-complexity trade-off; |c 6. The VC-dimension; |c 7. Non-uniform learnability; |c 8. The runtime of learning; |b Part II. From Theory to Algorithms: |c 9. Linear predictors; |b 10. Boosting; |c 11. Model selection and validation; |c 12. Convex learning problems; |c 13. Regularization and stability; |c 14. Stochastic gradient descent; |c 15. Support vector machines; |c 16. Kernel methods; |c 17. Multiclass, ranking, and complex prediction problems; |c 18. Decision trees; |c 19. Nearest neighbor; |c 20. Neural networks; |b Part III. Additional Learning Models: |c 21. Online learning; |c 22. Clustering; |c 23. Dimensionality reduction; |c 24. Generative models; |c 25. Feature selection and generation; |b Part IV. Advanced Theory: |c 26. Rademacher complexities; |c 27. Covering numbers; |c 28. Proof of the fundamental theorem of learning theory; |c 29. Multiclass learnability; |c 30. Compression bounds; |c 31. PAC-Bayes; |b Appendix A. Technical lemmas; |b Appendix B. Measure concentration; |b Appendix C. Linear algebra. | |
| 452 | | | |0 231141513 |t Understanding machine learning |o from theory to algorithms |f Shai Shalev-Shwartz,... Shai Ben-David,... |d 2014 |c New York, NY |n Cambridge University Press |s Institute of Mathematical Statistics Textbooks |y 978-1-107-29801-9 | |
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