Description

A key challenge in many AI systems is being able to handle (reason, plan or learn) problems with uncertainty. For instance, a self-driving car needs to handle imperfect visibility of the world and also uncertainty about the movements of other cars or obstacles. Similarly, in aggregation systems (e.g., Grab, Food Panda), positioning supply (taxis, delivery boys) at the right locations requires handling uncertainty about customer demand. Playing strategic games like Go, Chess etc. requires learning strategy that works against opponents whose strategy is not known in advance. This course will equip students with core concepts and practical experience in doing reasoning, learning and planning in the presence of uncertainty.

Learning Outcomes

Graduate Learning Outcomes

Disciplinary Knowledge, Critical thinking & problem solving, Innovation and enterprising skills, Collaboration and leadership, Communication, Self-directed learning

Competencies

Change Management, Computational Modelling, Intelligent Reasoning, Pattern Recognition Systems, Self-Learning Systems