News: August 2016, Second edition is now available! See buying section for more details.
A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.
Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.
- Publishers: Chapman and Hall/CRC
- Amazon (UK)
- Amazon (US)
Dr. Simon Rogers: is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches the masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human−computer interaction.
Prof. Mark Girolami: Mark Girolami holds the Chair of Statistics in the Department of Statistical Science at University College London (UCL). He is also Director of the Centre for Computational Statistics and Machine Learning at UCL, and holds a Professorial position in the Department of Computer Science at UCL. Prior to joining UCL Mark held the Chair of Computing and Inferential Science at the University of Glasgow. In 2011 he was elected to the Fellowship of the Royal Society of Edinburgh.
Matlab scripts mentioned in the text as well as data can be downloaded using the links below. In addition, the output of the various scripts can be seen here.
Update (August 2016): We are currently finalising all of the code (Matlab, Python and R) for the second edition. It will appear on Github in September 2016.
- Dropbox link to all code and data
- Link to missing plot_2D_gauss.m file
- For those people unable to use Dropbox, here is a local link to the code and data.
A solutions manual is available to qualifying adopters. Contact the publishers for more details.
- Moodle site for L4/M course at University of Glasgow (UoG students/staff only).
- Inference, Dynamics and Interaction group in the School of Computing Science at the University of Glasgow.
- School of Computing Science, University of Glasgow.
- Department of Statistics, University College London.
- Centre for Computational Statistics and Machine Learning, University College London.
- I have started compiling an errata to pick up any errors that fell through the proofing process (currently this is relevant to the first edition). These errors should be fixed in the second edition. Any additions gratefully received!
- Daryl Weir's Machine Learning teaching aid (JAR file). Daryl wrote this during his Masters year at the University of Glasgow. It allows the user to generate data and then apply various popular Machine Learning algorithms (KNN, Least squares regression, Support Vector Machines, K-means). If you use this, and find it useful, please let us know. For further information, contact Daryl.