- Hardcover: 480 pages
- Publisher: MIT Press (7 Sept. 2012)
- Language: English
- ISBN-10: 026201825X
- ISBN-13: 978-0262018258
- Product Dimensions: 17.8 x 2.1 x 22.9 cm
- Average Customer Review: 2.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: 246,137 in Books (See Top 100 in Books)
Foundations of Machine Learning (Adaptive Computation and Machine Learning Series) Hardcover – 7 Sep 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
About the Author
Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences and a Research Consultant at Google Research. Afshin Rostamizadeh is a Research Scientist at Google Research. Ameet Talwalkar is a National Science Foundation Postdoctoral Fellow in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley.
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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.