Understanding Machine Learning
From Theory to Algorithms
$66.99 (C)
- Authors:
- Shai Shalev-Shwartz, Hebrew University of Jerusalem
- Shai Ben-David, University of Waterloo, Ontario
- Date Published: May 2014
- availability: Available
- format: Hardback
- isbn: 9781107057135
$
66.99
(C)
Hardback
Other available formats:
eBook
Looking for an examination copy?
If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.
-
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, BerkeleyCustomer reviews
23rd Jan 2018 by Tork
I just want to find out the Exercise book answers of this book.
See all reviews05th 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
×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: Available
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
Instructor Resources
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact lecturers@cambridge.org.
Related Books
related journals
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×