Advanced Probabilistic Machine Learning
This page reflects the course material for the course instant that was given during fall 2019.
For the course instant given in fall 2021, all course information are provided in Studium https://uppsala.instructure.com/courses/53069.
For the course instant given in fall 2020, all course information are provided in Studium https://uppsala.instructure.com/courses/28106.
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 Structure
- Lectures: 11
- Problem solving sessions: 9
- Computer lab: 1 (mandatory)
- Mini project: 1 (mandatory)
- Exam: Oral exam (mandatory)
- Literature: A few different sources, all available online
- Language of instruction: English
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.