Pattern Recognition and Machine LearningThis is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. 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 selfcontained introduction to basic probability theory. 
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Review: Pattern Recognition and Machine Learning
User Review  Хотло Ширнууд  GoodreadsExcellent book with very detailed explanations on the fundamentals of the statistics and machine learning. Read full review
Review: Pattern Recognition and Machine Learning
User Review  John  GoodreadsDense but useful. Wish I hadn't sold my copy. Read full review