000			03198cam a22003015a 4500
001			18053648
003			OSt
005			20201128021505.0
008			140304t2014 nyua frb f001 0 eng d
020			_a9781107057135 (hardback)
020			_a1107057132 (hardback)
040			_aDLC _beng _cDLC _dDLC _dEG-ScBUE
082	0	4	_a006.31 _223 _bSHA
100	1		_aShalev-Shwartz, Shai. _938870
245	1	0	_aUnderstanding machine learning : _bfrom foundations to algorithms / _cShai Shalev-Shwartz, Jerusalem, Shai Ben-David.
260			_aNew York : _bCambridge University Press, _cc.2014.
300			_axvi, 397 p. : _bill. ; _c26 cm.
500			_aIndex : p. 395-397.
504			_aBibliography : p. 385-393.
505	8		_aMachine 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.
520			_a"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"--
650		7	_aMachine learning. _2BUEsh _92922
650		7	_aAlgorithms. _2BUEsh _913108
651			_2BUEsh
653			_bCOMSCI _cAugust2015 _cDecember2015
700	1		_aBen-David, Shai.
942			_2ddc
999			_c20531 _d20503