Pattern Recognition and Machine Learning(English, Paperback, Bishop Christopher M.)

Pattern Recognition and Machine Learning(English, Paperback, Bishop Christopher M.)

  • Bishop Christopher M.
Publisher:SpringerISBN 13: 9781493938438ISBN 10: 1493938436

Paperback & Hardcover deals ―

Amazon IndiaGOFlipkart ₹ 1447SnapdealGOSapnaOnlineGOJain Book AgencyGOBooks Wagon₹143Book ChorGOCrosswordGODC BooksGO

e-book & Audiobook deals ―

Amazon India GOGoogle Play Books GOAudible GO

* Price may vary from time to time.

* GO = We're not able to fetch the price (please check manually visiting the website).

Know about the book -

Pattern Recognition and Machine Learning(English, Paperback, Bishop Christopher M.) is written by Bishop Christopher M. and published by Springer-Verlag New York Inc.. It's available with International Standard Book Number or ISBN identification 1493938436 (ISBN 10) and 9781493938438 (ISBN 13).

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.