Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

ISBN: 9780521518147

Category: Computers

Page: 697

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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Bayesian Reasoning and Machine Learning
Language: en
Pages: 697
Authors: David Barber
Categories: Computers
Type: BOOK - Published: 2012-02-02 - Publisher: Cambridge University Press

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Bayesian Reasoning and Machine Learning
Language: en
Pages:
Authors: David Barber
Categories: Computers
Type: BOOK - Published: 2012-02-02 - Publisher: Cambridge University Press

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
Machine Learning
Language: en
Pages: 1160
Authors: Sergios Theodoridis
Categories: Computers
Type: BOOK - Published: 2020-02-19 - Publisher: Academic Press

Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression,
Bayesian Reinforcement Learning
Language: en
Pages: 146
Authors: Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar
Categories: Computers
Type: BOOK - Published: 2015-11-18 - Publisher:

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are
Machine Learning
Language: en
Pages: 1067
Authors: Kevin P. Murphy
Categories: Computers
Type: BOOK - Published: 2012-08-24 - Publisher: MIT Press

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict