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Understanding machine learning: from theory to algorithms

"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 transf…
New York, NY, USA, 2014


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

Tags

algorithms computers / computer vision & pattern recognition machine learning handapparate

Queries

Handapparate catalog - mediathek catalog - HGK catalog - Handapparate catalog - HGKplus

Full spec

CallNumber
408.2 SHAL
DateAdded
2016-09-20T14:51:33Z
DateModified
2016-09-20T14:52:15Z
ISBN
978-1-107-05713-5
Key
EK5IITUI
LibraryCatalog
Mediathek der Künste
NumPages
397
Publisher
Cambridge University Press
ShortTitle
Understanding machine learning