Pattern Recognition and Machine Learning

Chris Bishop is the Laboratory Director of Microsoft Research Cambridge and a Microsoft Distinguished Scientist. He is also a Fellow of Darwin College, Cambridge, and a Professor of Computer Science at the University of Edinburgh. He was named Fellow of the Royal Academy of Engineering in 2004, and Fellow of the Royal Society of Edinburgh in 2007. Chris graduated from Oxford with a BA in Physics and from the University of Edinburgh with a PhD in Theoretical Physics and a thesis on quantum field theory.

This is the first pattern recognition textbook to present the Bayesian viewpoint. Pattern Recognition and Machine Learning discusses approximate inference techniques, which allow for quick approximate responses in situations where exact answers are not possible. When no other books employ graphical models to machine learning, it uses them to characterize probability distributions. There is no presumption of prior understanding of pattern recognition or machine learning ideas. A working knowledge of multivariate calculus and basic linear algebra is necessary, as is some expertise with probabilities, however this is not required because the book offers a self-contained introduction to basic probability theory.

Author: Christopher M. Bishop

Link to buy: https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/

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