Artificial Intelligence (AI) and Machine Learning are often applied in situations characterised by various kinds of uncertainty, for example uncertainty in data measurements, missing data or uncertainty in our prior knowledge about the problem. This module will provide principles that enable AI to treat uncertainty consistently in inference, search, optimisation and learning.
Learning Outcomes
By the end of the module students should be able to:
conceptually understand frameworks for consistent treatment of uncertainty in AI
understand principles of inference and fitting to data in AI models under uncertainty
describe and understand randomised search and optimisation techniques utilised in AI
