I am pleased to announce the publication of my book, "Machine learning: a probabilistic perspective" (MIT Press 2012). This book provides a unified view of machine learning, based on probabilistic inference and graphical models. It is designed to be accessible to upper level undergraduates as well as beginning graduate students. In addition, it covers various important topics that are not in other ML textbooks, such as conditional random fields, convex and non-convex sparsity promoting priors, and deep learning. Further details can be found at
http://www.cs.ubc.ca/~murphyk/MLbook/index.html Some endorsements: "An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong and deep! A great starting point for any university student -- and a must have for anybody in the field." -- Prof. Jan Peters, Darmstadt University of Technology/ Max-Planck Institute for Intelligent Systems "An amazingly comprehensive survey of the field, covering both the basic theory as well as cutting edge research. Richly illustrated and loaded with examples and exercises. I will tell my students (and myself) to read this cover to cover!" -- Prof. Max Welling, U.C. Irvine "Prof. Murphy excels at unravelling 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." -- Dr John Winn, Microsoft Research. "This book does a really nice job explaining the basic principles and methods of machine learning from a Bayesian perspective. It will prove useful to statisticians interested in the current frontiers of machine learning as well as machine learners seeking a probabilistic foundation for their methods. It hits the 4 c's: clear, current, concise, and comprehensive, and it deserves a place alongside 'All of Statistics' and 'The Elements of Machine Learning' on the practical statistician's bookshelf." -- Dr Steven Scott, Google Quantitative Analysis Team. _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
