Pattern Recognition and Machine Learning by Christopher M. Bishop presents approximate inference algorithms that allow fast approximate answers in conditions where actual solutions will not be feasible. It uses graphical models to describe probability distributions when no different books apply graphical models to machine learning.
No earlier knowledge of pattern recognition or machine learning ideas is assumed. The dramatic growth in sensible functions for machine studying over the last ten years has been accompanied by many essential developments in the underlying algorithms and techniques. For instance, Bayesian methods have grown from a specialist niche to turn out to be mainstream, whereas graphical models have emerged as a general framework for describing and applying probabilistic techniques.
The practical applicability of Bayesian methods has been vastly enhanced by the development of a variety of approximate inference algorithms comparable to variational Bayes and expectation propagation, while new models based on kernels have had a major influence on both algorithms and applications.
This completely new textbook reflects these current developments whereas providing a complete introduction to the fields of sample recognition and machine learning. It is geared toward advanced undergraduates or first-12 months PhD students, in addition to researchers and practitioners. No earlier information of pattern recognition or machine studying ideas is assumed.
Familiarity with multivariate calculus and basic linear algebra is required, and expertise in the use of probabilities could be helpful though not important because the book includes a self-contained introduction to basic probability theory. The book is suitable for programs on machine studying, statistics, computer science, signal processing, computer vision, knowledge mining, and bioinformatics.
In depth help is supplied for course instructors, together with greater than 400 exercises, graded in line with difficulty. Example solutions for a subset of the workout routines can be found from the book internet site, while solutions for the rest could be obtained by instructors from the publisher. The book is supported by quite a lot of extra material, and the reader is encouraged to visit the book web site for the latest information.
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