03883nam a2200469 i 4500 000718160 UkCbUP 20241003091924.0 20151005020622.0 m|||||o||d|||||||| cr|||||||||||| 130717s2014||||enk o ||1 0|eng|d NEZPRACOVANÝ IMPORT 9781107298019 (ebook) 9781107057135 (hardback) UkCbUP eng rda UkCbUP Q325.5 .S475 2014 006.3/1 23 Shalev-Shwartz, Shai, author. Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada. Cambridge : Cambridge University Press, 2014. 1 online resource (xvi, 397 pages) : digital, PDF file(s). text txt rdacontent computer c rdamedia online resource cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015). 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. 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 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. elektronické knihy fd186907 czenas electronic books eczenas Machine learning. Algorithms. Ben-David, Shai, author. Print version: 9781107057135 https://knihovna.vse.cz/zdroje/cambridge-core-books-and-textbooks/?info Informace o práci s e-knihami https://zdroje.vse.cz/login?url=https://doi.org/10.1017/CBO9781107298019 Plný text CAMBRIDGE EB PhDinfra NEPOSILAT EB