Mon compte
Se connecter Inscription
Tous
Panier
En Vedette
Livres de fiction
Livres de non-fiction
Livres pour enfants
Livres rares
Musique
DVD et Blu-Ray
Video Games
Catégorie
Listes de favoris
Panier
Tous
Livraison gratuite
Trustpilot
Nous sommes Neutres au Carbone

Understanding Machine Learning Shai Shalev-Shwartz (Hebrew University of Jerusalem)

Understanding Machine Learning par Shai Shalev-Shwartz (Hebrew University of Jerusalem)

Understanding Machine Learning Shai Shalev-Shwartz (Hebrew University of Jerusalem)

€56.00
État - Très bon état
Disponible en seulement 1 exemplaire(s)
Très bon état

Résumé

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.

Understanding Machine Learning Résumé

Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz (Hebrew University of Jerusalem)

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.

Understanding Machine Learning Avis

'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Germany
'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University
'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, Berkeley

À propos de Shai Shalev-Shwartz (Hebrew University of Jerusalem)

Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.

Sommaire

1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 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.

Informations supplémentaires

GOR009137965
9781107057135
1107057132
Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz (Hebrew University of Jerusalem)
Shai Shalev-Shwartz (Hebrew University of Jerusalem)
Occasion - Très bon état
Relié
Cambridge University Press
2014-05-19
410
N/A
La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Il s'agit d'un livre d'occasion - par conséquent, il a été lu par quelqu'un d'autre et il présente des signes d'usure et d'utilisation antérieure. Dans l'ensemble, nous nous attendons à ce qu'il soit en très bon état, mais si vous n'êtes pas entièrement satisfait, veuillez prendre contact avec nous.