04522nam a2200421 i 4500 40939 BD-DhAAL 20230220215106.0 230220r20222014nyua b 001 0 eng 2014001779 9781107057135 (hardback) 1107057132 (hardback) 9781107512825 DLC eng DLC rda DLC BD-DhAAL pcc 006.31 23 Shalev-Shwartz, Shai. 53683 Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz and Shai Ben-David First south asia edition 2015 New York, NY, USA ; India : Cambridge University Press, 2014. [Reprinted 2022] xvi, 397 pages : illustrations ; 26 cm. Includes bibliographical references (pages 385-393) and index. 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"-- CSE Book Finder 40939, 40940, 40941, 40942, 40943 Machine learning. Algorithms. COMPUTERS / Computer Vision & Pattern Recognition. bisacsh 53684 Ben-David, Shai. 53685 Ayesha Abed Library General Stacks ddc BK 44780 44780 0 0 ddc 0 006_310000000000000_SHA 0 73171 BRACUL BRACUL GEN 2023-02-07 Book Finder 1427.71 5 73 006.31 SHA 3010040940 2026-05-19 2025-04-22 2025-04-22 2 1427.71 2023-02-07 BK 0 0 ddc 0 006_310000000000000_SHA 0 73172 BRACUL BRACUL GEN 2023-02-07 Book Finder 1427.71 7 91 006.31 SHA 3010040941 2026-05-20 2025-11-18 2025-11-18 3 1427.71 2023-02-07 BK 0 0 ddc 0 006_310000000000000_SHA 0 73173 BRACUL BRACUL GEN 2023-02-07 Book Finder 1427.71 6 81 006.31 SHA 3010040942 2025-07-17 2024-11-12 4 1427.71 2023-02-07 BK 0 0 ddc 0 006_310000000000000_SHA 0 73174 BRACUL BRACUL GEN 2023-02-07 Book Finder 1427.71 6 92 006.31 SHA 3010040939 2026-05-17 2025-12-22 1 1427.71 2023-02-07 BK 0 0 ddc 0 006_310000000000000_SHA 0 73175 BRACUL BRACUL GEN 2023-02-07 Book Finder 1427.71 8 77 006.31 SHA 3010040943 2026-02-18 2026-02-02 5 1427.71 2023-02-07 BK