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A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.
What People are Saying About This
I have used Introduction to Machine Learning for several years in my graduate Machine Learning course. The book provides an ideal balance of theory and practice, and with this third edition, extends coverage to many new state-of-the-art algorithms. I look forward to using this edition in my next Machine Learning course.
This volume is both a complete and accessible introduction to the machine learning world. This is a 'Swiss Army knife' book for this rapidly evolving subject. Although intended as an introduction, it will be useful not only for students but for any professional looking for a comprehensive book in this field. Newcomers will find clearly explained concepts and experts will find a source for new references and ideas.
Ethem Alpaydin's Introduction to Machine Learning provides a nice blending of the topical coverage of machine learning (à la Tom Mitchell) with formal probabilistic foundations (à la Christopher Bishop). This newly updated version now introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and learning to rank) to students and researchers of this critically important and expanding field.
"A few years ago, I used the first edition of this book as a reference book for a project I was working on. The clarity of the writing, as well as the excellent structure and scope,impressed me. I am more than pleased to find that this second edition continues to be highly informative and comprehensive, as well as easy to read and follow." Radu State Computing Reviews
The MIT Press