Last modified: 15 Aug 2024 09:46
Computational Intelligence covers a wide range of issues that developed in parallel with, or in competition to, symbolic AI. The major constituents of the field are bio-inspired computing “ which deals with an ever expanding number of biologically related techniques “ and fuzzy logic “ which deals with reasoning under conditions of vagueness”. In this course we will explore a number of topics that are core to Computational Intelligence (e.g. neural nets and evolutionary computing) and these will lead into some state-of-the-art approaches (such as fuzzy model-based reasoning and learning).
| Study Type | Undergraduate | Level | 4 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Offshore | Sustained Study | No |
| Co-ordinators |
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A selection of topics spanning a range of Computational Intelligence approaches under the following headings:
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Programming exercise including 1,500-word report |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate knowledge and understanding of basic concepts of Nature inspired computing, Fuzzy methods, and Model-based Reasoning |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Apply | Use Model-based Reasoning tools (e.g. the Morven Fuzzy Qualitative Reasoning System) and Nature Inspired Computing tools (e.g. Artificial Immune System software) |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in Computational Intelligence |
| Assessment Type | Summative | Weighting | 70 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
2-hour exam |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate knowledge and understanding of basic concepts of Nature inspired computing, Fuzzy methods, and Model-based Reasoning |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Apply | Use Model-based Reasoning tools (e.g. the Morven Fuzzy Qualitative Reasoning System) and Nature Inspired Computing tools (e.g. Artificial Immune System software) |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in Computational Intelligence |
| Procedural | Evaluate | Critically evaluate outcomes and alternatives |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate knowledge and understanding of basic concepts of Nature inspired computing, Fuzzy methods, and Model-based Reasoning |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in Computational Intelligence |
| Procedural | Apply | Use Model-based Reasoning tools (e.g. the Morven Fuzzy Qualitative Reasoning System) and Nature Inspired Computing tools (e.g. Artificial Immune System software) |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Evaluate | Critically evaluate outcomes and alternatives |
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