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.

The enormous increase in practical machine learning applications over the last ten years has been followed by numerous significant advances in the underlying algorithms and methodologies. Bayesian methods, for example, have progressed from a specialist niche to the mainstream, whereas graphical models have evolved as a general framework for expressing and using probabilistic techniques. The introduction of a variety of approximation inference algorithms such as variational Bayes and expectation propagation has substantially improved the practical usability of Bayesian approaches, while new models based on kernels has had a considerable impact on both algorithms and applications.

This entirely new textbook incorporates these recent advancements while also providing a thorough introduction to the topics of pattern recognition and machine learning. It is intended for advanced undergraduates, first-year PhD students, researchers, and practitioners. 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.

Machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics courses will benefit from this book. Course teachers receive extensive support, including over 400 activities of varying difficulty. Example solutions for a subset of the exercises are provided on the book's website, while solutions for the remainder can be requested from the publisher by instructors. Pattern Recognition and Machine Learning is supplemented with a wealth of additional content, and readers are encouraged to visit the book's website for the most up-to-date information. It is regarded as one of the best books on computer vision.

Author: Christopher M. Bishop

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

Ratings: 4.6 out of 5 stars (from 639 reviews)

Best Sellers Rank: #112,328 in Books

#11 in Bioinformatics (Books)

#15 in Computer Vision & Pattern Recognition

#41 in Artificial Intelligence (Books)