COMP64101: Reasoning and Learning under Uncertainty (Semester A, 2025/26 Academic Year)

Learning outcomes:

1. Describe the fundamental concepts of uncertainty quantification
2. Describe the use of uncertainty quantification for active learning, Bayesian optimisation and out-of-distribution learning
3. Analyse the differences among statistical inference approaches
4. Explain the models and algorithms commonly used in probabilistic graphical models, state space models, Bayesian neural networks and Gaussian processes
5. Apply an advanced uncertainty quantification model to a data-driven application using auto-differentiation tools and probabilistic programs
6. Critically assess technologies and analyse their suitability for specific application scenarios