Last modified: 10 Oct 2025 12:16
Computational intelligence (CI) is the subfield of artificial intelligence (AI) covering the biologically and linguistically inspired AI techniques, including fuzzy systems, neural networks, evolutionary computation, and Bayesian modelling. This course introduces the fundamentals of CI including probability theory and fuzzy logic, as well as the modern neural network architectures including convolutional neural networks and transformers. We will also discuss the recent developments and future trends in CI.
| 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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The course will cover a selection of topics spanning a range of Computational Intelligence approaches under the following headings:
• Probability theory, Bayesian reasoning and modelling.
• Hidden Markov models.
• Fundamentals of game theory.
• Fuzzy systems, including fuzzy logic and operations.
• Genetic algorithms and swarm intelligence.
• Artificial neural networks, including Bayesian neural networks, convolutional neural networks, recurrent neural networks, and transformers.
• Generative AI models, including generative adversarial networks and diffusion models.
• Advanced training techniques, including contrastive learning and reinforcement learning.
• Recent advances and trends in Computational Intelligence, including green AI and ethical AI.
Information on contact teaching time is available from the course guide.
| 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 computational intelligence |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Apply | Use nature inspired computing tools and methodologies such as artificial neural networks and reinforcement learning to solve practical tasks |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in computational intelligence |
| 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 computational intelligence |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Apply | Use nature inspired computing tools and methodologies such as artificial neural networks and reinforcement learning to solve practical tasks |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in computational intelligence |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate knowledge and understanding of basic concepts of computational intelligence |
| Procedural | Analyse | Analyse problems and select appropriate concepts and models to solve them |
| Procedural | Apply | Use nature inspired computing tools and methodologies such as artificial neural networks and reinforcement learning to solve practical tasks |
| Procedural | Apply | Use knowledge and understanding of appropriate principles and guidelines to synthesise solutions to tasks in computational intelligence |
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