Further resources
While selected material from the resources may form part of the compulsory reading, the resources below are generally not required reading and you do not need to procure the books. For some of the books, direct links to the library copies are available on the resource list page on Learn.
- Pattern Recognition and Machine Learning
Christopher Bishop
Springer
(available at the University library, some online material available here) - Probabilistic Graphical Models : Principles and Techniques
Daphne Koller and Nir Friedman
MIT Press 2009
(available at the University library) - Bayesian networks : an introduction
Timo Koski and John Noble
John Wiley 2009
(available at the University library) - Graphical models
Steffen L. Lauritzen
Oxford University Press 1996
(available at the University library) - Probability and Statistics
Morris DeGroot and Mark Schervish
Pearson, 4th Edition - Information Theory, Inference, and Learning Algorithms
David J.C. MacKay
Cambridge University Press 2003
(available online) - The Matrix Cookbook
Petersen and Pedersen
(available online) - Graphical models and message-passing algorithms: Some introductory lectures
Martin J. Wainwright
(available online) - An Introduction to Probabilistic Graphical Models (incomplete draft)
Michael Jordan
(parts available online here and here) - Independent Component Analysis
Aapo Hyvarinen, Juha Karhunen, and Erkki Oja
(electronic copy available at the University library) - Monte Carlo Statistical Methods
Christian Robert and George Casella
Springer 2004 - Bayesian data analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin
CRC Press 2013
(available at the University library) - Mathematics for Machine Learning
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Cambridge University Press 2020
(available online)