Pattern Recognition and Machine Learning

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. 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.

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

• | Author: Christopher M. Bishop
• | Publisher: Springer-Verlag New York Inc.
• | Publication Date: Aug 17, 2006
• | Number of Pages:
• | Language:
• | Binding: Hardback
• | ISBN-13: 9780387310732
• | ISBN-10: 0387310738