000			01822nam a22002417a 4500
003			IN-KoAU
005			20251127171655.0
008			251127b ii ||||| |||| 00| 0 eng d
020			_a9781107512825
040			_beng
041			_aEnglish
082			_a006.31/SHA/U
100			_aShalev-Shwartz, Shai _923470
245			_aUnderstanding Machine Learning _bfrom theory to algorithms _cShai Shalev-Shwartz and, Shai Ben-David
260			_aLondon _bCambridge _c2019
300			_axvi, 397 p.
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.
650			_aMachine learning. _920567
650			_aAlgorithms. _923100
650			_aCOMPUTERS / Computer Vision & Pattern Recognition. _923471
700			_aBen-David, Shai _923472
942			_cBOOKS ECE
999			_c31736 _d31736