Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. 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 required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. No previous knowledge of pattern recognition or machine learning concepts is assumed. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. ![]() ![]() This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning.
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