This textbook addresses pattern recognition and machine learning from a computer science perspective. Christopher M. Bishop provides a comprehensive introduction to probabilistic models, with special emphasis on Bayesian methods and graphical models. The book is intended for advanced undergraduate and first-year master’s students, researchers, and professionals without prior knowledge of pattern recognition or machine learning.
Product description
Pattern recognition has its origins in engineering, while machine learning comes from computer science. Bayesian methods have evolved from a specialized topic into a mainstream technique. Graphical models provide a general framework for describing and applying probabilistic models. In addition, this book highlights recent developments such as approximate inference algorithms and kernel-based models, thereby significantly improving practical applicability.
Specifications
- Author: Christopher M. Bishop
- Series: Information Science and Statistics
- Publisher: Springer-Verlag New York Inc.
- Publication date: 2006-08-17
- Number of pages: 778
- ISBN: 9780387310732
- Subject: Computer vision
- BISAC: COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition
About the author
Chris Bishop is a Microsoft Distinguished Scientist and Laboratory Director at Microsoft Research Cambridge. He is also a Professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge. Bishop has been recognized as a Fellow of the Royal Academy of Engineering and the Royal Society of Edinburgh. His background is in theoretical physics and quantum field theory.