000			03488nam a2200361 i 4500
001			40939
003			BD-DhAAL
005			20230220215106.0
008			230220r20222014nyua b 001 0 eng
010			_a 2014001779
020			_a9781107057135 (hardback)
020			_a1107057132 (hardback)
020			_a9781107512825
040			_aDLC _beng _cDLC _erda _dDLC _dBD-DhAAL
042			_apcc
082	0	0	_a006.31 _223
100	1		_aShalev-Shwartz, Shai. _953683
245	1	0	_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz and Shai Ben-David
250			_aFirst south asia edition 2015
260			_aNew York, NY, USA ; _aIndia : _bCambridge University Press, _c2014. [Reprinted 2022]
300			_axvi, 397 pages : _billustrations ; _c26 cm.
504			_aIncludes bibliographical references (pages 385-393) and index.
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"--
526			_aCSE
650		0	_aMachine learning.
650		0	_aAlgorithms.
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition. _2bisacsh _953684
700	1		_aBen-David, Shai. _953685
852			_aAyesha Abed Library _cGeneral Stacks
942			_2ddc _cBK
999			_c44780 _d44780