Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

The dramatic growth of practical applications for machine learning over the past decade has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have gone from being a specialized niche to becoming mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a variety of approximate inference algorithms, such as Variational Bayesian and Expectation Propagation, while new kernel-based models have had a significant impact on both algorithms and applications. .

This brand new textbook reflects these recent developments by providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduate students or first-year graduate students, as well as researchers and professionals. No prior knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience using probability would be helpful, but not essential, as the book includes a separate introduction to basic probability theory.

The book is suitable for courses in machine learning, statistics, computer science, signal processing, computer vision, data mining and bioinformatics. Extensive support is provided to the course instructors, including over 400 exercises, categorized by difficulty. Examples of solutions for a subset of the exercises are available on the book’s website, while solutions for the rest can be obtained from the faculty at the publisher. The book is supported by a wide range of additional material and the reader is encouraged to visit the book’s website for the latest information.

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