Advanced Probabilistic Machine Learning

Education facts

Code: 1RT705

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.

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.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Advanced Probabilistic Machine Learning

  Uppsala University

  Uppsala

  Start date: 31 August 2026

  End date: 1 November 2026

  Pace of study: 33 %