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 (notably the lab and the mini project). The practical part will be implemented using Python.

Course syllabus

Course Structure

• Lectures: 11
• Exercise sessions: 9
• Mini project: 1 (mandatory)
• Computer lab: 1 (mandatory)
• Exam: Oral exam (mandatory)
• Literature: A few different sources, all available online
• Language of instruction: English

Schedule

The schedule can be found here. Links to an external site. All scheduled teaching activities will be conducted online via zoom due to the covid-19 situation. We will use the same zoom meeting room for all teaching activities, which you find here with the topic name "Advanced probabilistic machine learning, HT2020". It is required that you are logged in to Studium and registered to the course to see the meeting link. The lectures will be recorded and made available after each lecture.

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 slots for each exercise session in the schedule. Choose the one that fits best with your schedule. The exercise sessions will not be recorded.

Formalities

The course is 5 credits. 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.

Teachers

Niklas Wahlström Links to an external site. (course responsible, lecturer)	Andreas Lindholm Links to an external site. (lecturer)	Thomas Schön Links to an external site. (lecturer)
David Widmann Links to an external site. (teaching assistant)	Carl Andersson Links to an external site. (teaching assistant)	Jens Sjölund Links to an external site. (teaching assistant)