Bayesian Reasoning and Machine Learning by David Barber - PDF free download eBook

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  • Published: Sep 05, 2015
  • Reviews: 98

Brief introduction:

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...

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Details of Bayesian Reasoning and Machine Learning

ISBN
9780521518147
Publication date
Age range
18+ Years
Book language
ENG
Pages
650
Format
PDF, EPUB, FB2, FB3
Quality
Normal quality scanned pages
Dimensions
7.60 (w) x 9.88 (h) x 1.46 (d)
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Some brief overview of this book

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 masters 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.

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A few words about book author

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

dergraduates and masters 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.

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EPUB

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PDF

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