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.

  • 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)
  • 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)