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    Understanding Machine Learning: From Theory to Algorithms (Hardback) By (author) Shai Shalev-Shwartz, By (author) Shai Ben-David

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    DescriptionMachine 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.


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  • Full bibliographic data for Understanding Machine Learning

    Title
    Understanding Machine Learning
    Subtitle
    From Theory to Algorithms
    Authors and contributors
    By (author) Shai Shalev-Shwartz, By (author) Shai Ben-David
    Physical properties
    Format: Hardback
    Number of pages: 424
    Width: 177 mm
    Height: 253 mm
    Thickness: 28 mm
    Weight: 910 g
    Language
    English
    ISBN
    ISBN 13: 9781107057135
    ISBN 10: 1107057132
    Classifications

    BIC E4L: COM
    B&T Book Type: NF
    Nielsen BookScan Product Class 3: S10.3T
    Warengruppen-Systematik des deutschen Buchhandels: 16320
    B&T General Subject: 229
    LC subject heading:
    B&T Merchandise Category: COM
    Ingram Subject Code: XG
    Libri: I-XG
    LC subject heading:
    DC22: 006.3/1
    LC subject heading:
    DC22: 006.31
    BIC subject category V2: UYQM
    BISAC V2.8: COM016000
    LC subject heading: , ,
    DC23: 006.31
    LC classification: Q325.5 .S475 2014
    Thema V1.0: UYQM, UYQP
    Illustrations note
    47 b/w illus. 123 exercises
    Publisher
    CAMBRIDGE UNIVERSITY PRESS
    Imprint name
    CAMBRIDGE UNIVERSITY PRESS
    Publication date
    19 May 2014
    Publication City/Country
    Cambridge
    Author Information
    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.
    Review quote
    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 Scholkopf, Max Planck Institute for Intelligent Systems Advance praise: '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
    Table of contents
    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.