Intelligent Systems Techniques
Module code: 802G5
Level 7 (Masters)
15 credits in spring teaching
Teaching method: Laboratory, Lecture
Assessment modes: Coursework, Unseen examination
This module will introduce you to the range of knowledge representation techniques used in contemporary Artificial Intelligence, and give you an understanding of their various strengths and weaknesses.
Module learning outcomes
- Discuss theories of knowledge and related developments in Artificial Intelligence in the context of the historic development of the field.
- Demonstrate systematic understanding of several established knowledge representation and reasoning methods such as sentential logic, semantic networks, ontologies, fuzzy systems, and Bayesian networks.
- Identify, critically assess, and implement computational techniques that are used in common applications of Artificial Intelligence such as automated reasoning, problem-solving, game-playing, or route-finding.
- Demonstrate the ability to engage with academic literature and articulate complex issues related to theories of machine intelligence.