Pattern Recognition and Machine Learning - Christopher M. Bishop

738 pages / 25 x 15 cm / English A definitely recommended book to start tackling machine learning by lecturer This 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 self-contained introduction to basic probability theory. Review From the reviews: #34;This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group. #34; John Maindonald for the Journal of Statistical Software #34;In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields. #34; (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) Order @toagraphicbook