Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop

ISBN 10: 1493938436 / ISBN 13: 9781493938438

Published by Springer New York, 2016

About this Item

Grade 3 out 5 points. Book has wear on cover and pages. May have personalized notes/names, stickers/labels. Has no markings on pages. May not include extra materials like access codes, CDs, accessories, etc. All orders ship by next business day! We are a small company and very thankful for your business! Softcover reprint of the original 1st ed.

Seller Inventory # mon0000055677

Bibliographic Details

• Title: Pattern Recognition and Machine Learning (Information Science and Statistics)
• Publisher: Springer New York
• Publication Date: 2016
• Language: English
• ISBN 10: 1493938436
• ISBN 13: 9781493938438
• Binding: Paperback
• Condition: Very Good

About this title

Synopsis

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.

About the Author

​Chris Bishop is a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, and in 2007 he was elected Fellow of the Royal Society of Edinburgh. 

Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.