000			03277nam a2200301 4500
001			016708076
003			BD-ChPU
005			20161109111816.0
008			140304s2014 nyua b 001 0 eng
020			_a9781107057135
040			_aBD-ChPU _cBD-ChPU _dBD-ChPU _beng
082			_a 006.31 S528u 2014 _222
100			_aShalev-Shwartz, Shai.
245			_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz, Shai Ben-David.
250			_aFirst edition.
260			_aNew York : _bCambridge University Press, _c2014. _c2016.[Reprinted]
300			_axvii, 449 pages : _billustrations ; _c26 cm.
500			_aFormerly CIP.
504			_aIncludes bibliographical references and index.
505			_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"-- |c Provided by publisher.
526			_aComputer Science and Engineering.
650			_aMachine learning. _93635
650			_a Algorithms.
650			_aCOMPUTERS / Computer Vision & Pattern Recognition. _2 bisacsh
700			_aBen-David, Shai.
942			_2ddc _cBK
999			_c4552 _d4552