Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz

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Características del artículo

Estado
En buen estado: Libro que se ha leído pero que está en buen estado. Daños mínimos en la tapa, ...
Publish Year
2014
Book Title
Understanding Machine Learning: From Theory to Algorithms
ISBN
9781107057135
Categoría

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Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1107057132
ISBN-13
9781107057135
eBay Product ID (ePID)
171820749

Product Key Features

Number of Pages
410 Pages
Publication Name
Understanding Machine Learning : from Theory to Algorithms
Language
English
Publication Year
2014
Subject
Algebra / General, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Mathematics, Computers
Author
Shai Ben-David, Shai Shalev-Shwartz
Format
Hardcover

Dimensions

Item Height
1.1 in
Item Weight
32.2 Oz
Item Length
10.2 in
Item Width
7.2 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2014-001779
Reviews
Advance praise: '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 Schölkopf, Max Planck Institute for Intelligent Systems, "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 Schlkopf, Max Planck Institute for Intelligent Systems
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Table Of Content
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.
Synopsis
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., 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., 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.
LC Classification Number
Q325.5 .S475 2014

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