Lecturer (wks 1-5): Chris Williams
Lecturer (wks 6-11): Michael Gutmann
Teaching assistant: Vaidotas Simkus

Recent announcements

Apr 3, 2023 The Course Enhancement Questionnaire is available on Learn -> Have your Say or via this link. You will need to be logged into your Office 365 account to access the survey. All responses are anonymous.
Mar 31, 2023 Uploaded solution to tutorial 8.
Mar 24, 2023 Uploaded solution to tutorial 7.
Mar 19, 2023 Uploaded solution to tutorial 6.
Mar 10, 2023 Uploaded solution to tutorial 5.


The course provides you with a thorough understanding of statistical/probabilistic machine learning methods that are used in a wide range of different fields with a focus on unsupervised learning. The course covers four main topics:

  1. probabilistic graphical models,
  2. exact inference,
  3. learning,
  4. approximate inference and learning.

The course material is available here and optional material here.



  • The course is assessed by a written exam at the end of the course (75% of final mark) and three quizzes during the semester (25% of final mark). There is no assignment.
  • The quizzes check your engagement with the course and are typically easier than the exam.
  • The exam date is announced here and past exams are available here (search for probabilistic modelling and reasoning).
  • PMR follows the Common Marking Scheme of the university and the school-wide academic conduct policy.


  • The quizzes open 1 week prior to their due date. They are time-limited and you will you have 1 attempt for each quiz.
  • The maximal time to complete a quiz is 60 minutes. The 60 mins will allow you to consult the slides or tutorials, but you will still need to study the material beforehand. Otherwise, you will likely run out of time.
  • The quizzes are available on gradescope. Please access it via Learn.
  • The quizzes will be autosaved and you can revise them as often as you want but once you have started a quiz, you will have to finish it within 60 minutes after you started.
  • When a quiz “closes at 4pm”, it means that you can work on it until 4pm.

:heavy_check_mark: Test quiz: Opens Monday Feb 06 at 9am, closes Friday Feb 10 at 4pm. This is to check that you can access gradescope and allows you to familiarise yourself with the types of questions asked in the quizzes. It takes less than five minutes to complete. Please get in touch with the TA in case of access problems or questions.

:heavy_check_mark:Quiz 1: Opens Friday Feb 10 at 9am, closes Friday Feb 17 at 4pm (end of week 5). The quiz is on directed and undirected graphical models. It covers the material on the slides until (and including) “Undirected Graphical Models II”, and the exercises discussed in tutorials 1 and 2.

:heavy_check_mark:Quiz 2: Opens Friday March 3 at 9am, closes Friday March 10 at 4pm (end of week 7). The quiz is on inference and message passing. It covers the material on the slides “Factor Graphs” and “Exact Inference”, and the exercises discussed in tutorial 4.

:heavy_check_mark:Quiz 3 Opens Friday March 17 at 9am, closes Friday March 24 at 4pm (end of week 9). The quiz is on learning. It covers the slides “Basics of Model-Based Learning”, “Factor Analysis and Independent Component Analysis”, and Tutorial 6, Exercises 1-10. Note: you will have to do some calculations by hand. So please have pen and paper ready.