Bayesian Reasoning and Machine Learning
Author: David Barber
Publisher: Cambridge University Press
ISBN: 9780521518147
Category: Computers
Page: 697
View: 685
Language: en
Pages: 697
Pages: 697
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Language: en
Pages:
Pages:
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
Language: en
Pages: 1160
Pages: 1160
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,
Language: en
Pages: 146
Pages: 146
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
Language: en
Pages: 1067
Pages: 1067
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
