Bayesian Reasoning and Machine Learning

The book is designed to appeal to students with only a modest mathematical background in undergraduate calculus and linear algebra. No formal computer science or statistical background is required to follow the book, although a basic familiarity with probability, calculus and linear algebra would be useful. The book should appeal to students from a variety of backgrounds, including Computer Science, Engineering, applied Statistics, Physics, and Bioinformatics that wish to gain an entry to probabilistic approaches in Machine Learning. In order to engage with students, the book introduces fundamental concepts in inference using only minimal reference to algebra and calculus. More mathematical techniques are postponed until as and when required, always with the concept as primary and the mathematics secondary.

The concepts and algorithms are described with the aid of many worked examples. The exercises and demonstrations, together with an accompanying MATLAB toolbox, enable the reader to experiment and more deeply understand the material. The ultimate aim of the book is to enable the reader to construct novel algorithms. The book therefore places an emphasis on skill learning, rather than being a collection of recipes. This is a key aspect since modern applications are often so specialised as to require novel methods. The approach taken throughout is to describe the problem as a graphical model, which is then translated into a mathematical framework, ultimately leading to an algorithmic implementation in the BRMLtoolbox.

The book is primarily aimed at final year undergraduates and graduates without significant experience in mathematics. On completion, the reader should have a good understanding of the techniques, practicalities and philosophies of probabilistic aspects of Machine Learning and be well equipped to understand more advanced research level material.