PMR 2025

Lecturer: Michael Gutmann  
Teaching assistant: Foteini Dervisi 
Lecture/tutorial times and locations: please see here.
Office hours (for November): please contact Foteini Dervisi 

Content

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 five main topics:

1. probabilistic graphical models,
2. exact inference,
3. actions and decision making,
4. learning,
5. approximate inference and learning.

The course material is available here and optional material here. The lecture recordings are available on Learn. We use Piazza for course Q&A.

Assessment

Overview

• 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.

Quizzes

• 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.
• Marking scheme:  Each quiz consists of N components, each of which is marked individually using an algorithmically defined test-criterion. If the component passes the test, a mark is awarded, otherwise no marks are awarded. The total of each quiz is the aggregation of the individual marks.

✓ Test quiz: Opens Monday September 29 at 1pm (week 3), closes Friday October 3 at 9am (week 3). 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.

✓ Quiz 1: Opens Friday October 3 at noon (week 3), closes Friday October 17 at noon (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.

Quiz 2: Opens Friday October 17 at noon (week 5), closes Friday October 31 at noon (week 7). The quiz is on inference and message passing. It covers the material on the slide “Exact Inference”, the inference self-study questions, and the inference exercises discussed in tutorials 3 and 4. 

Quiz 3: Opens Friday October 31 at noon (week 7), closes Friday November 14 at noon (week 9). The quiz is on learning. It covers the slides “Basics of Model-Based Learning”, “Factor Analysis and Independent Component Analysis”, "Intractable Likelihood Functions", the corresponding self-study exercises, and Tutorial 6. Note: you will have to do some calculations by hand. Please have pen and paper, and a calculator ready.