Christopher Bishop

Christopher Bishop

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.

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Books Authored by Christopher Bishop

Pattern Recognition and Machine Learning

Post date: 28 Nov 2020
This textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners.
Publisher: Springer-Verlag GmbH
Publication date: 17 Aug 2006
Document Type: Textbook
 
Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Post date: 28 Nov 2020
This textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners.
Publisher: Springer-Verlag GmbH
Publication date: 17 Aug 2006
Document Type: Textbook


[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book
 
[Early Access Version] Model-Based Machine Learning

[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book


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