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“Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, ISBN-13: 978-0471495178” has been added to your cart. View cart
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Bayesian Reasoning and Machine Learning 1st Edition by David Barber, ISBN-13: 978-0521518147
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Home Computing Bayesian Reasoning and Machine Learning 1st Edition by David Barber, ISBN-13: 978-0521518147
An Introduction to Statistical Learning: with Applications in R by Gareth James, ISBN-13: 978-1461471370
An Introduction to Statistical Learning: with Applications in R by Gareth James, ISBN-13: 978-1461471370 $50.00 Original price was: $50.00.$14.99Current price is: $14.99.
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C# 10 in a Nutshell: The Definitive Reference 1st Edition by Joseph Albahari, ISBN-13: 978-1098121952
C# 10 in a Nutshell: The Definitive Reference 1st Edition by Joseph Albahari, ISBN-13: 978-1098121952 $50.00 Original price was: $50.00.$22.99Current price is: $22.99.

Bayesian Reasoning and Machine Learning 1st Edition by David Barber, ISBN-13: 978-0521518147

$50.00 Original price was: $50.00.$14.99Current price is: $14.99.

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SKU: bayesian-reasoning-and-machine-learning-1st-edition-by-david-barber-isbn-13-978-0521518147 Category: Computing Tags: Bayesian Reasoning and Machine Learning 1st Edition by David Barber, David Barber, ISBN-10: 0521518148, ISBN-13: 978-0521518147
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Description

Bayesian Reasoning and Machine Learning 1st Edition by David Barber, ISBN-13: 978-0521518147

[PDF eBook eTextbook]

  • Publisher: ‎ Cambridge University Press; 1st edition (March 12, 2012)
  • Language: ‎ English
  • 735 pages
  • ISBN-10: ‎ 0521518148
  • ISBN-13: ‎ 978-0521518147

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.

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

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