03250nam a22003137a 4500 OSt 20260401111705.0 260401s2014 nyua frb 001 0 eng 2014001779 9781107057135 18053648 DLC eng DLC rda DLC clrauoh 006.31 Shalev-Shwartz, Shai. author. Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, Shai Ben-David New York, NY, USA : Cambridge University Press, 2014 xvi, 397 pages : illustrations. texto txt rdacontent no mediado n rdamedia volumen nc rdacarrier Includes bibliographical references (pages 385-393) and index. 1. Introduction -- Part I. 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 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. "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"-- Provided by publisher. Machine learning. Algorithms. COMPUTERS / Computer Vision & Pattern Recognition. bisacsh Ben-David, Shai. author. ddc BOOK 0 7009 7009 2026-04-01 11:03:57 35672011248 0 006.31 S528u 2014 35672011248 1 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00 2026-04-01 11:03:00 35672011249 0 006.31 S528u 2014 35672011249 2 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00 2026-04-01 11:03:00 35672011250 0 006.31 S528u 2014 35672011250 3 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00 2026-05-25 07:57:35 35672011251 0 006.31 S528u 2014 35672011251 4 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00 2026-04-01 11:03:00 35672011252 0 006.31 S528u 2014 35672011252 5 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00 2026-04-01 11:03:00 35672011253 0 006.31 S528u 2014 35672011253 6 2026-04-01 BOOK 0 ddc 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-11-19 102930.00