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Understanding Machine Learning
From Theory to Algorithms

$64.99 (P)

  • Date Published: May 2014
  • availability: In stock
  • format: Hardback
  • isbn: 9781107057135

$ 64.99 (P)
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  • 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.

    • Provides a principled development of the most important machine learning tools
    • Describes a wide range of state-of-the-art algorithms
    • Promotes understanding of when machine learning is relevant, what the prerequisites for a successful application of ML algorithms are, and which algorithms to use for any given task
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    Reviews & endorsements

    "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 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

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    Product details

    • Date Published: May 2014
    • format: Hardback
    • isbn: 9781107057135
    • length: 410 pages
    • dimensions: 260 x 183 x 28 mm
    • weight: 0.91kg
    • contains: 47 b/w illus. 123 exercises
    • availability: In stock
  • 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.

  • Resources for

    Understanding Machine Learning

    Shai Shalev-Shwartz, Shai Ben-David

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  • Authors

    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, University of Waterloo, Ontario
    Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.

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