Advanced Probabilistic Machine Learning
Course, Master's level, 1RT705
Expand the information below to show details on how to apply and entry requirements.
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 31 August 2026–1 November 2026
- Language of instruction
- English
- Entry requirements
-
120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus, Statistical Machine Learning, a course in several variable analysis and a course in introductory programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2026
- Application code
- UU-11801
Admitted or on the waiting list?
- Registration period
- 27 July 2026–6 September 2026
- Information on registration from the department
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 31 August 2026–1 November 2026
- Language of instruction
- English
- Entry requirements
-
120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus, Statistical Machine Learning, a course in several variable analysis and a course in introductory programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 27 July 2026–6 September 2026
- Information on registration from the department
About the course
This is an advanced course in machine learning, focusing on modern probabilistic/Bayesian methods, including Bayesian linear regression, generative models, and graphical models. Additionally, it covers methods for exact and approximate inference in these models, such as Monte Carlo methods, variational inference, and the Laplace approximation.
The course encompasses both theory (e.g., derivations and proofs) and practice. The practical part will be implemented using Python.