Pattern Recognition and Machine Learning

This textbook introduces the fields of pattern recognition and machine learning, emphasizing the Bayesian viewpoint. It presents approximate inference algorithms that allow for fast approximate answers in situations where exact answers are not feasible. The book uses graphical models to describe probability distributions, a method not commonly applied in other machine learning texts. No prior knowledge of pattern recognition or machine learning concepts is assumed, though familiarity with multivariate calculus and basic linear algebra is required. The book includes a self-contained introduction to basic probability theory.