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).

Obs: This course instance is last time 1RT003 will be offered. From 2025 onwards only 1RT705 will be offered. Three re-exams will be given for 1RT003 after this course instance.

Course Structure

Lectures: 14 (1RT003), 10 (1RT705)
Exercise sessions: 11 (1RT003), 8 (1RT705)
Mini project: 1 (mandatory for all)
Peer-review: 1 (mandatory for all)
Computer lab: 1 (mandatory only for 1RT003)
Exam: 1 (mandatory for all, shorter exam for 1RT705)
Literature: A few different sources, all available online
Helpdesk: with focus on the miniproject
Language of instruction: English

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.

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 from week 38 on and are marked with "H" in the schedule. 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).

Teachers


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

Paul Häusner (teaching assistant)	Ziwei Luo (teaching assistant)	Lovisa Eriksson (teaching assistant)	Aleix Nieto Juscafresa (teaching assistant)