Bayesian Reasoning and Machine Learning
"With approachable text, examples, exercises, guidelines for teachers,   a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and   Machine Learning by David Barber provides everything needed for your   machine learning course. Only students not included."Jaakko Hollmén, Aalto University"Barber has done a commendable job in presenting important concepts   in probabilistic modeling and probabilistic aspects of machine learning.   The chapters on graphical models form one of the clearest and most concise   presentations I have seen. The book has wide coverage of probabilistic   machine learning, including discrete graphical models, Markov decision   processes, latent variable models, Gaussian process, stochastic and deterministic   inference, among others. The material is excellent for advanced undergraduate   or introductory graduate course in graphical models, or probabilistic machine   learning. The exposition throughout the book uses numerous diagrams and   examples, and the book comes with an extensive software toolbox - these will   be immensely helpful for students and educators. It's also be a great resource   for self-study for people with background knowledge in basic probability and   linear algebra."Arindam Banerjee, University of Minnesota"I repeatedly get unsolicited comments from my students that the contents   of this book have been very valuable in developing their understanding   of machine learning. This book appeals to readers from many backgrounds,   and is driven by examples of machine learning in action. Despite   maintaining that level of accessibility, it does not avoid covering   areas that are of practical use but often harder to explain. Neither   does it shun a proper understanding of why the methods work; each   chapter is a pointer to the overall probabilistic framework upon which   these machine learning methods depend. My students praise this book   because it is both coherent and practical, and  because it makes fewer   assumptions regarding the reader's statistical knowledge and confidence   than many books in the field."Amos Storkey, University of Edinburgh"This book is an exciting addition to the literature on machine learning   and graphical models. What makes it unique and interesting is that it   provides a unified treatment of machine learning and related fields   through graphical models, a framework of growing importance and   popularity. Another feature of this book lies in its smooth transition   from traditional artificial intelligence to modern machine learning. The   book is well-written and truly pleasant to read. I believe that it will   appeal to students and researchers with or without a solid mathematical   background."Zheng-Hua Tan, Aalborg University