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)