Book,
Bayesian Reasoning and Machine Learning
(2010)
Abstract
The last decade has seen considerable growth in interest in Artificial Intelligence and Machine Learning. In the broadest sense, these fields aim to ‘learn something useful’ about the environment within which the organism operates. How gathered information is processed leads to the development of algorithms – how to process high dimensional data and deal with uncertainty. In the early stages of research in Machine Learning and related areas, similar techniques were discovered in relatively isolated research communities. Whilst not all techniques have a natural description in terms of probability theory, many do, and it is the framework of Graphical Models (a marriage between graph and probability theory) that has enabled the understanding and transference of ideas from statistical physics, statistics, machine learning and information theory. To this extent it is now reasonable to expect that machine learning researchers are familiar with the basics of statistical modelling techniques.
This book concentrates on the probabilistic aspects of information processing and machine learning. Certainly no claim is made as to the correctness or that this is the only useful approach. Indeed, one might counter that this is unnecessary since “biological organisms don’t use probability theory”. Whether this is the case or not, it is undeniable that the framework of graphical models and probability has helped with the explosion of new algorithms and models in the machine learning community. One should also be clear that Bayesian viewpoint is not the only way to go about describing machine learning and information processing. Bayesian and probabilistic techniques really come into their own in domains where uncertainty is a necessary consideration.
