- Series: Adaptive Computation and Machine Learning
- Hardcover: 432 pages
- Publisher: The MIT Press (August 17, 2012)
- Language: English
- ISBN-10: 026201825X
- ISBN-13: 978-0262018258
- Product Dimensions: 7 x 1.1 x 9 inches
- Shipping Weight: 2.4 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #212,728 in Books (See Top 100 in Books)
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Foundations of Machine Learning (Adaptive Computation and Machine Learning) Hardcover – August 17, 2012
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In my opinion, the content of the book is outstanding in terms of clarity of discourse and the variety of well-selected examples and exercises. The enlightening comments provided by the author at the end of each chapter and the suggestions for further reading are also important features of the book. The concepts and methods are presented in a very clear and accessible way and the illustrative examples contribute substantially to facilitating the understanding of the overall work.―Computing Reviews
A solid, comprehensive, and self-contained book providing a uniform treatment of a very broad collection of machine learning algorithms and problems. Foundations of Machine Learning is an essential reference book for corporate and academic researchers, engineers, and students.―Corinna Cortes, Head of Google Research, NY
Finally, a book that is both broad enough to cover many algorithmic topics of machine learning and mathematically deep enough to introduce the required theory for a graduate level course. Foundations of Machine Learning is a great achievement and a significant contribution to the machine learning community.―Yishay Mansour, School of Computer Science, Tel Aviv University
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In contrast, this book gives an unbiased presentation of machine learning with solid theoretical justifications. It discusses the principles behind the design of learning algorithms by introducing and using the most modern tools and concepts in learning theory. This helps answering many fundamental questions.
The presentation is concise and the topics covered very broad. They include the presentation of several of the most well known binary classification algorithms, multi-class classification, regression, ranking, on-line learning, reinforcement learning, structured prediction, learning theory, and many other topics. In particular, there is a nice and concise presentation of SVMs and boosting. The appendix introduces all the main tools needed, including a brief introduction to convex optimization.
I strongly recommend this book to students and researchers. It gives a very modern presentation covering all the main topics in learning, which can serve as a reference for everyone. Perhaps more importantly, it helps us analyze and understand machine learning.