Bayesian Reasoning and Machine Learning

David Barber

ISBN 10: 0521518148 / ISBN 13: 9780521518147

Published by Cambridge University Press, 2012

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9780521518147

Bibliographic Details

• Title: Bayesian Reasoning and Machine Learning
• Publisher: Cambridge University Press
• Publication Date: 2012
• Language: English
• ISBN 10: 0521518148
• ISBN 13: 9780521518147
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 44D millimeters 250 millimeters width by 175 millimeters height by 44 millimeters depth
• Weight: 1430 grams

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.