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UNDERSTANDING MACHINE LEARNING: FROM THEORY TO ALGORITHMS 2014 (H)
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ISBN: 9781107057135
類別: 電腦Computer Science & Engineering
出版社: CAMBRIDGE UNIVERSITY PRESS
作者: SHALEV-SHWARTZ
年份: 2014
裝訂別: 精裝
頁數: 410
定價: 1,480
售價: 1,332
原幣價: USD 64.99
狀態: 正常
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.

> 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

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.
Springer 國外現貨
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