Pattern Recognition and Machine Learning - No Cost Library

Pattern Recognition and Machine Learning

   Author(s): Christopher M. Bishop  

Publisher: Springer, Year: 2006

                                        

 Description: 

This is the first textbook to present the Bayesian perspective on pattern recognition. The book presents approximate inference algorithms that allow quick, approximate responses in situations where precise responses are not feasible. To explain probability distributions, it uses graphical models while no other books apply graphical models to machine learning. No prior knowledge of pattern recognition or principles for machine learning is presumed. Familiarity with multivariate calculus and basic linear algebra is necessary, and some experience with the use of probabilities would be helpful but not important since the book provides a self-contained introduction to basic probability theory.

Numerous significant advances in the fundamental algorithms and techniques have followed the rapid growth in practical applications for machine learning over the last ten years. For example, Bayesian approaches have evolved to become popular from a specialist niche while graphic models have emerged as a general framework for explaining and applying probabilistic techniques. The functional applicability of Bayesian methods has been greatly improved by the development of a variety of approximate inference algorithms such as variational Bayes and the propagation of expectations, whereas modern kernel-based models have dramatically affected both algorithms and applications. It is intended for advanced graduates or first year PhD students, as well as for researchers and practitioners. No prior knowledge of pattern recognition or principles for machine learning is presumed. The book is suitable for courses on machine learning , statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Familiarity with multivariate calculus and basic linear algebra is needed, and some experience in the use of probabilities would be helpful but not necessary as the book contains a self-contained introduction to basic probability theory. Extensive help is given to teachers of the course, including over 400 activities, graded according to difficulty. Example solutions are available from the book website for a subset of activities, while solutions for the remainder can be purchased from the publisher by teachers. A lot of additional material supports the book, and the reader is encouraged to visit the book website for the latest content. Coming soon:* For students, worked solutions to a subset of exercises available on a public website (for exercises marked 'www' in the text)* For teachers, worked solutions to remaining exercises from the Springer website*

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