Pattern recognition and machine learning

The field of pattern recognition has undergone substantial development over the last ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. The practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had significant impact on both algorithms and applications. This new text book reflects these recent developments while providing a solid grounding in the basic concepts of pattern recognition and machine learning.It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Familiarity with multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. TOC:Introduction.- Probability distributions.- Graphical models.- Mixture models and EM.- Approximate inference.- Continuous latent variables.- Sequential data.- Linear models for regression.- Linear models for classification.- Kernel methods.- Sparse kernal machines.- Neural networks.