03845cam a2200373 i 4500 13101 13101 OSt 20230916110515.0 140304s2014 nyua b 001 0 eng 9781107512825 DLC eng DLC rda DLC pcc Q325.5 .S475 2014 006.3/1 23rd ed. SHA COM016000 bisacsh Shalev-Shwartz, Shai. 3367 Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada. New York, NY, USA : Cambridge University Press, 2014. xvi, 397 pages : illustrations ; 26 cm text rdacontent unmediated rdamedia volume rdacarrier 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"-- Provided by publisher. Machine learning. 3368 Algorithms. 3369 COMPUTERS / Computer Vision & Pattern Recognition. bisacsh 3370 Ben-David, Shai. 3371 Cover image http://assets.cambridge.org/97811070/57135/cover/9781107057135.jpg 7 cbc orignew 1 ecip 20 y-gencatlg ddc Book 0 0 ddc 0 0 AEF AEF 2023-09-16 20 876.00 8642 2023-09-16 1095.00 2023-09-16 Book