Publications

Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal & Chen Soon Ong (2020)

From the authors: "We wrote a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.

The book is available at published by Cambridge University Press (published April 2020).

We split the book into two parts:

• Mathematical foundations
• Example machine learning algorithms that use the mathematical foundations

We aimed to keep this book fairly short, so we don’t cover everything."

We will keep PDFs of this book freely available.

Bayesian Reasoning and Machine Learning by David Barber (2012)

The book is available in hardcopy from Cambridge University Press.

The publishers have kindly agreed to allow the online version to remain freely accessible.

If you wish to cite the book, please use

@BOOK{barberBRML2012,
author = {Barber, D.},
title= {{Bayesian Reasoning and Machine Learning}},
publisher = {{Cambridge University Press}},
year = 2012}

Please refer to this page as http://www.cs.ucl.ac.uk/staff/d.barber/brml/

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, by Nello Cristianini & John Shawe-Taylor (2013)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Kernel Methods for Pattern Analysis, by John Shawe-Taylor & Nello Christianini (2004)

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.