Bayesian Reasoning and Machine Learning (Hardcover)

David Barber

ISBN 10: 0521518148 / ISBN 13: 9780521518147

Published by Cambridge University Press, Cambridge, 2012

About this Item

Hardcover. Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online. This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780521518147

Bibliographic Details

• Title: Bayesian Reasoning and Machine Learning (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2012
• Language: English
• ISBN 10: 0521518148
• ISBN 13: 9780521518147
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

About the Author

David Barber is Reader in Information Processing in the Department of Computer Science, University College London.