03351nam a2200325 i 450000100060000000300090000600500170001500800410003201000170007302000290009002000260011902000180014504000380016304200080020108200150020910000260022424501060025025000340035626000870039030000460047750400670052350511060059052011580169652600080285465000220286265000160288465000640290070000210296485200400298540939BD-DhAAL20230220215106.0230220r20222014nyua     b    001 0 eng    a  2014001779  a9781107057135 (hardback)  a1107057132 (hardback)  a9781107512825  aDLCbengcDLCerdadDLCdBD-DhAAL  apcc00a006.312231 aShalev-Shwartz, Shai.10aUnderstanding machine learning :bfrom theory to algorithms /cShai Shalev-Shwartz and Shai Ben-David  aFirst south asia edition 2015  aNew York, NY,  USA ;aIndia :bCambridge University Press,c2014. [Reprinted 2022]  axvi, 397 pages :billustrations ;c26 cm.  aIncludes bibliographical references (pages 385-393) and index.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.  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"--  aCSE 0aMachine learning. 0aAlgorithms. 7aCOMPUTERS / Computer Vision & Pattern Recognition.2bisacsh1 aBen-David, Shai.  aAyesha Abed LibrarycGeneral Stacks