Course content

This is an advanced course in machine learning, focusing on modern probabilistic/Bayesian methods: Bayesian linear regression, generative models, and graphical models, as well as methods for exact and approximate inference in such models: Monte Carlo methods, variational inference, and the Laplace approximation. The course also covers the necessary probability theory.

The course includes theory (e.g., derivations and proofs) as well as practice. The practical part will be implemented using Python.

Course Structure

Lectures: 10
Exercise sessions: 9
Course project: 1
Peer-review: 1
Computer lab: 1
Exam: 1
Literature: A few different sources, all available online
Helpdesk: with focus on the miniproject

Click on the link above to get further info about each course element.

Exercise sessions

During the exercise sessions, the teaching assistant will give a short introduction of the material related to that session and during the session provide insight and comments on the solutions. Except for this, you are supposed to be active and work with the suggested exercises and the teaching assistants will be available for questions and discussion. Take this opportunity to interact! The exercises will be available ahead of time before the session. There are two time-slots for each exercise session in the schedule. Choose the one that fits best with your schedule.

Helpdesk

Helpdesks will start in week 38. You are welcome to come with questions related to the course. The focus and the priority of these office hours will be the course project.

Course evaluation and course report from last year

Course evaluation and course report for the previous course edition can be found here.

Policy on generative AI

In this course, you may use generative AI as a tool to facilitate learning and to seek knowledge and deepening within the course. It is also permitted to use generative AI to produce material that is included in the answers to examination tasks, provided that all use is clearly reported. Remember that you are fully responsible for the content of everything you submit, and must be able to stand by it and, if necessary, argue for it. An exception is made for AI tools for language checking. The use of such tools to only check spelling and pure grammatical errors in an existing text does not need to be reported.

Teachers


Jens Sjölund (course responsible, lecturer)	Sara Hamis (lecturer)	Thomas Schön (lecturer)	Antonio Horta Ribeiro (lecturer)	Jodie Cochrane (teaching assistant)

David Vävinggren (teaching assistant)	Ziwei Luo (teaching assistant)	Lovisa Eriksson (teaching assistant)	Stina Brunzell (teaching assistant)	Anton O-Nils (teaching assistant)