Synopses & Reviews
andlt;Pandgt;This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. andlt;Iandgt;Foundations of Machine Learningandlt;/Iandgt; fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.andlt;/Pandgt;andlt;Pandgt;The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar. andlt;/Pandgt;
Review
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. The MIT Press
Review
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. < b=""> Corinna Cortes <> , Head of Google Research, NY
Review
andlt;Pandgt;andquot;A solid, comprehensive, and self-contained book providing a uniform treatment of a very broad collection of machine learning algorithms and problems. andlt;Iandgt;Foundations of Machine Learningandlt;/Iandgt; is an essential reference book for corporate and academic researchers, engineers, and students.andquot;andlt;Bandgt;--Corinna Cortesandlt;/Bandgt;, Head of Google Research, NYandlt;/Pandgt; The MIT Press The MIT Press
Review
andlt;Pandgt;andquot;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. andlt;Iandgt;Foundations of Machine Learningandlt;/Iandgt; is a great achievement and a significant contribution to the machine learning community.andquot;andlt;Bandgt; -- Yishay Mansourandlt;/Bandgt;, School of Computer Science, Tel Aviv University andlt;/Pandgt; The MIT Press
Review
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. < b=""> Yishay Mansour <> , School of Computer Science, Tel Aviv University
Synopsis
Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
Synopsis
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
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.