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230220r20222014nyua b 001 0 eng |
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|a 2014001779
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|a 9781107057135 (hardback)
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|a 1107057132 (hardback)
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|a 9781107512825
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|a Shalev-Shwartz, Shai.
|9 53683
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| 245 |
1 |
0 |
|a Understanding machine learning :
|b from theory to algorithms /
|c Shai Shalev-Shwartz and Shai Ben-David
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| 250 |
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|a First south asia edition 2015
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| 260 |
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|a New York, NY, USA ;
|a India :
|b Cambridge University Press,
|c 2014. [Reprinted 2022]
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| 300 |
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|a xvi, 397 pages :
|b illustrations ;
|c 26 cm.
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| 504 |
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|a Includes bibliographical references (pages 385-393) and index.
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| 505 |
8 |
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|a Machine 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.
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| 520 |
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|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"--
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| 526 |
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|a CSE
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| 541 |
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|a Book Finder
|e 40939, 40940, 40941, 40942, 40943
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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0 |
|a Algorithms.
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| 650 |
|
7 |
|a COMPUTERS / Computer Vision & Pattern Recognition.
|2 bisacsh
|9 53684
|
| 700 |
1 |
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|a Ben-David, Shai.
|9 53685
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| 852 |
|
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|a Ayesha Abed Library
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