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