Advanced Probabilistic Machine Learning
Advanced course on modern probabilistic and Bayesian approaches to machine learning.
Instructor: Jens Sjölund, Sara Hamis, Thomas B. Schön, Antônio H. Ribeiro, and others
Overview
This advanced course (1RT705, 5 hp) covers modern probabilistic and Bayesian approaches to machine learning, including Bayesian linear regression, Bayesian networks, latent variable models, Gaussian processes, and methods for exact and approximate inference.
Format
- 10 lectures
- 8 exercise sessions
- 1 mandatory mini-project report with peer review
- Written exam
- Optional helpdesk sessions
Prerequisites
120 credits including Statistical Machine Learning, Probability and Statistics, Linear Algebra II, single and multivariable calculus, and basic programming.