While selected material from the resources below may form part of the compulsory reading, the material is generally not required reading. In particular, you will not need to procure any books for the course.

Additional support for the lectures

This is a collection of additional material that may help you to better understand the lectures. Advanced material, sometimes going well beyond the scope of PMR, is marked with a ⚠.

Background

Directed graphical models

Undirected graphical models

Expressive power and comparison between directed and undirected graphical models

Factor graphs

Exact inference

Learning, factor analysis, and ICA

Score matching and other methods to estimate unnormalised models

Sampling and Monte Carlo

Variational inference and EM algorithm

Books

This is a list with general and specialised machine learning books. For some of the books, direct links to the library copies are available on the resource list page on Learn. The library’s search portal is here.

  • 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
    (available at the University library)
  • 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
  • Introducing Monte Carlo Methods with R
    Christian Robert and George Casella
    Springer 2010
    (available at the University library)
  • 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)
  • An Introduction to Sequential Monte Carlo
    Nicolas Chopin and Omiros Papaspiliopoulos
    Springer 2020
    (available at the University library)
  • Art Owen
    Monte Carlo theory, methods and examples
    (available online)
  • Probabilistic machine learning book series by Kevin Murphy
    (available online)