Intelligent Systems Techniques (802G5)
Intelligent Systems Techniques
Module 802G5
Module details for 2025/26.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module provides a general introduction to Artificial Intelligence (AI) for postgraduate students. Taking an epistemological perspective, students engage with theories of knowledge and intelligence and get to know related knowledge representation methods and reasoning techniques that are common in AI applications. Alongside the theoretical aspects, students also get hands-on experience in applying and/or implementing the methods and techniques in computer programs.
(Students choosing this module must be able to program, either through successful completion of the Programming through Python module in the autumn semester, or by having equivalent practical experience.
This option is unavailable to those students who previously completed a Computer Science and Artificial Intelligence degree at this 5XÉçÇøÊÓƵ).
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.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 50.00% | |
| Coursework components. Weighted as shown below. | ||
| Project | T2 Week 10 | 100.00% |
| Multiple Choice questions | Semester 2 Assessment | 50.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Laboratory | 1 hour | 11111111110 |
| Spring Semester | Lecture | 1 hour | 22222222222 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Chris Thornton
Assess convenor
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