03198cam a22003015a 4500 18053648 OSt 20201128021505.0 140304t2014 nyua frb f001 0 eng d 9781107057135 (hardback) 1107057132 (hardback) DLC eng DLC DLC EG-ScBUE 006.31 23 SHA Shalev-Shwartz, Shai. 38870 Understanding machine learning : from foundations to algorithms / Shai Shalev-Shwartz, Jerusalem, Shai Ben-David. New York : Cambridge University Press, c.2014. xvi, 397 p. : ill. ; 26 cm. Index : p. 395-397. Bibliography : p. 385-393. Machine generated contents note: 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; 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"-- Machine learning. BUEsh 2922 Algorithms. BUEsh 13108 BUEsh COMSCI August2015 December2015 Ben-David, Shai. ddc 20531 20503 0 0 ddc 0 0 Baccah MAIN MAIN LOW 2015-08-16 Purchase 1013.00 21759 2 16 006.31 SHA 000030815 2025-07-15 00:00:00 2019-04-09 1266.00 BB