Artificial and machine intelligence are increasingly reliant on models of the world that account for uncertainty. For example, when an intelligent agent such as a robot explores its environment, it is constantly trying to reason and make inferences based on observations and prior beliefs. The observations are unreliable, and prior beliefs may be only approximate or weighted beliefs; the agent needs to be able to account for all the information it has to estimate the outcome if it takes a certain course of action. Similarly, in medicine, we are constantly faced with problems of disease diagnosis (or trying to establish the cause of a disease), and we only have probabilistic information about the most likely disease (or the most likely cause). This module will look at all the tools and principles behind progress in these and other challenging problems.
Learning Outcomes
By the end of the module students should be able to:
Demonstrate a sound understanding of logic and probability and their role in artificial intelligence.
Formulate decision making problems using probabilistic and logic-based methods
Apply randomized methods for decision making such as Monte Carlo, MCMC, and Monte Carlo Tree Search
Model and analyse complex data using probabilistic graphical models
