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Reviewer: Radu State

A comprehensive and Bayesian-oriented introduction to machine learning is presented in this book. Writing from a probabilistic viewpoint, Murphy manages to provide a fresh and intellectually stimulating overview of the subject. There are very many strong points in the book. The author strikes a good balance between theory and practice. Some machine learning books focus too much on the theory and leave out the applied content, while others are biased toward tools and practical applications, without explaining the rationale behind a specific method. This informative, sound, and self-contained book has the right mix of theory, background knowledge, and concrete applications. With more than 1,000 pages and 28 chapters, the book covers such machine learning concepts as clustering, classification, graphical models, kernel-based learning approaches, and expectation-maximization (EM) methods. These constitute the building blocks of the theory behind machine learning. While I don't have enough room to give a chapter-by-chapter presentation of this book, I specifically recommend chapter 26, which covers the learning of graphical models, a typically underrepresented topic in existing machine learning books. As an additional bonus, the author provides a MATLAB package for all of the described methods, making it easy for readers to develop and validate their own experiments. The book targets advanced undergraduate or graduate students who are taking a machine learning class or are looking for a solid introduction to this area. Applied researchers will also benefit from the practical and real-world examples and code samples. All of these readers will find this a valuable and relevant addition to their bookshelves. Online Computing Reviews Service