Bayesian Reasoning and Machine Learning

by David Barber

• ISBN13: 9780521518147
• ISBN10: 0521518148
• Format: Hardcover
• Copyright: 2012-03-12
• Publisher: Cambridge University Press

Summary

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.

Table of Contents

Preface

Inference in Probabilistic Models

Probabilistic reasoning

Basic graph concepts

Belief networks

Graphical models

Efficient inference in trees

The junction tree algorithm

Making decisions

Learning in Probabilistic Models

Statistics for machine learning

Learning as inference

Naive Bayes

Learning with hidden variables

Bayesian model selection

Machine Learning

Machine learning concepts

Nearest neighbour classification

Unsupervised linear dimension reduction

Supervised linear dimension reduction

Linear models

Bayesian linear models

Gaussian processes

Mixture models

Latent linear models

Latent ability models

Dynamical Models

Discrete-state Markov models

Continuous-state Markov models

Switching linear dynamical systems

Distributed computation

Approximate Inference

Sampling

Deterministic approximate inference

Appendix. Background mathematics

Bibliography

Index

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