Course content

This is an advanced course in machine learning, focusing on modern probabilistic/Bayesian methods: Bayesian linear regression, Bayesian networks, latent variable models and Gaussian processes, as well as methods for exact and approximative inference in such models. The course also contains necessary probability theory and methods for data dimensionality reduction.

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

Two versions

The course exists in two versions - 1RT705 (5 course credits) and 1RT003 (7.5 course credits).

If you are registered as a PhD student, the course codes are FTN0092 (5 course credits) and FTN0204 (7.5 course credits).

Course Structure

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

Schedule

Campus teaching

The course will be taught on campus. Recorded lectures from previous years are available, but be aware that the course structure has gone through some changes. Also the order of the lectures is slightly different in each year. The exercise sessions will be given on campus.

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

A helpdesk will start from week 36 on. You are welcome to come with questions related to the course. The focus and the priority of these office hours will be the mini-project.

Formalities

The course is 5 credits for 1RT705 and 7.5 credits for 1RT003. Entry requirements are: 120 credits, including Statistical Machine Learning, Probability and Statistics, Linear Algebra II, Single Variable Calculus, a course in multivariable calculus and one basic programming course.

Course evaluation and course report 2023

Course evaluation and course report for the 2023 course version can be found here (login required).

Course evaluation and course report 2022

Course evaluation and course report for the 2022 course version can be found here (login required).

Teachers


Niklas Wahlström (course responsible, lecturer)	Jens Sjölund (lecturer)	Zheng Zhao (lecturer)	Thomas Schön (lecturer)

Daniel Gedon (teaching assistant lecturer)	Paul Häusner (teaching assistant)	Ziwei Luo (teaching assistant)	Lovisa Eriksson (teaching assistant)