000			03487nam a22003618i 4500
001			CR9781107298019
003			UkCbUP
005			20221102214119.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			130717s2014||||enk o ||1 0|eng|d
020			_a9781107298019 (ebook)
020			_z9781107057135 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.S475 2014
082	0	0	_a006.3/1 _223
100	1		_aShalev-Shwartz, Shai, _eauthor. _966904
245	1	0	_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xvi, 397 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
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			_aMachine 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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, 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		0	_aMachine learning. _91831
650		0	_aAlgorithms. _93390
700	1		_aBen-David, Shai, _eauthor. _966905
776	0	8	_iPrint version: _z9781107057135
856	4	0	_uhttps://doi.org/10.1017/CBO9781107298019
942			_cETB
999			_c81793 _d81793