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
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 Assistant Professor in the Machine Learning Department at Carnegie Mellon University.
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