CS5016 Uncertainty in Artificial Intelligence
This module covers reasoning and decision making in the presence of uncertainty. It introduces probabilities and probabilistic reasoning, approximate inference (Monte Carlo methods), Bayesian Networks and different types of Markov models. Students will learn the relevant theoretical concepts and gain practical experience in developing solutions to real problems.
This module is offered in 2025-26.
Aims
The aims of this module are:
- To give an overview of AI practice.
- To cover fundamental methods in AI.
Learning Outcomes
On successful completion of this module, the student should:
- Be aware of the underlying principles of of optimisation, reasoning with uncertainty.
- Be able to implement fundamental techniques of optimisation, reasoning with uncertainty, logic and knowledge representation, and AI search.
Syllabus
This module covers practical design and implementation of AI techniques, covering areas:
- Quantifying and reasoning with uncertainty.
- Optimisation.
- Probabilistic reasoning, approximate inference (Monte Carlo methods)
- Bayesian Networks and different types of Markov models.
It is shown how to implement AI ideas in software and how to evaluate such implementation.
Compulsory Elements
This module has no compulsory elements beyond those common to all modules (mark of 4 in each assessment component).
Module Delivery
- Nguyen Dang
- Lei Fang (support)