Pattern
Recognition and Machine Learning
This leading textbook provides a comprehensive introduction to the
fields of pattern recognition and machine learning. It is aimed at advanced
undergraduates or first-year PhD students, as well as researchers and
practitioners. No previous knowledge of pattern recognition or machine
learning concepts is assumed. This is the first machine learning textbook
to include a comprehensive coverage of recent developments such as probabilistic
graphical models and deterministic inference methods, and to emphasize a
modern Bayesian perspective. It is suitable for courses on machine
learning, statistics, computer science, signal
processing, computer vision, data mining, and bioinformatics. This hard
cover book has 738 pages in full colour, and there are 431 graded exercises
(with solutions available below). Extensive support is provided for course
instructors.
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