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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.Read more
- 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
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 SystemsSee more reviews
"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
23rd Jan 2018 by Tork
I just want to find out the Exercise book answers of this book.See all reviews
05th Jul 2018 by Wellhopelove007
It's a good book, of course, and it's hard to understand.so I want the answer of the exercises. thanks !
02nd Aug 2018 by Mtnlv
Great book, it is possible to get also the solutions for the exercises?
16th Mar 2019 by Rafaelespericueta
One of the very best intros to machine learning, if you're interested in the mathematical foundations. This requires some mathematical maturity, but given that the book is remarkably clear and complete.
Review was not posted due to profanity×
- 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
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
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
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
Appendix A. Technical lemmas
Appendix B. Measure concentration
Appendix C. Linear algebra.
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