Larry Holder
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.
John W. Sheppard
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.
Endorsement
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.
Hilario Gómez-Moreno, IEEE Senior Member, University of Alcalá, Spain
From the Publisher
This volume is a complete and accessible introduction to the machine learning world. This is a 'Swiss Army knife' book for this rapidly evolving subject.
Hilario Gómez-Moreno, IEEE Senior Member, University of Alcalá, Spain
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.
Larry Holder, Professor of Electrical Engineering and Computer Science, Washington State University