Machine Learning_ A Probabilistic Perspective_Kevin P. Murphy.pdf

Machine Learning A Probabilistic Perspective Kevin P. Murphy “An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong, and deep! A great starting point for any univer- sity student—and a must-have for anybody in the field.” Jan Peters, Darmstadt University of Technology; Max-Planck Institute for Intelligent Systems “Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real-world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data.” John Winn, Microsoft Research “This is a wonderful book that starts with basic topics in statistical modeling, culminating in the most ad- vanced topics. It provides both the theoretical foun- dations of probabilistic machine learning as well as practical tools, in the form of MATLAB code. The book should be on the shelf of any student interested in the topic, and any practitioner working in the field.” Yoram Singer, Google Research “This book will be an essential reference for practitio- ners of modern machine learning. It covers the basic concepts needed to understand the field as a whole, and the powerful modern methods that build on those concepts. In Machine Learning, the language of prob- ability and statistics reveals important connections be- tween seemingly disparate algorithms and strategies. Thus, its readers will become articulate in a holistic view of the state-of-the-art and poised to build the next generation of machine learning algorithms.” David Blei, Princeton University machine learning Machine Learning A Probabilistic Perspective Kevin P. Murphy Today’s Web-enabled deluge of electronic data calls