Intelligent Systems Techniques (802G5Z)
Intelligent Systems Techniques
Module 802G5Z
Module details for 2024/25.
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.
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 | VACATION Week 5 | 100.00% |
| Multiple Choice questions | Summer Vacation Week 4 Thu 13:30 | 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.
Dr Hsi-Ming Ho
Assess convenor
/profiles/519387
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