Christopher M. Bishop

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

Pattern Recognition and Machine Learning

Hugh Bishop

Foundations and Concepts

Deep Learning

`Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist

Neural Networks for Pattern Recognition

Pattern Recognition and Machine Learning by Christopher M. Bishop

Wolfgang Maass

Presents a spectrum of current research in pulsed neural networks and includes work from some of the key scientists in the field.

Christopher M. Bishop

About

Books

Join the club