Advanced Artificial Intelligence
(MRes) Advanced Artificial Intelligence
Entry for 2025
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course learning outcomes
Comprehend the key aspects of a range of recent artificial intelligence methodologies, related computational or mathematical concepts, and ethical issues, in order to propose and implement appropriate and effective technical solutions
Demonstrate critical awareness of challenges and possible negative implications
of applying artificial intelligence or computational analysis methods to a
particular problem or dataset and creatively propose mitigation strategies.
Synthesise knowledge from several sources to propose an appropriate
computational artificial intelligence approach for a specified problem showing
creativity and self-direction.
Identify key methodological concepts in published research and critically
contrast these against those in other works.
Construct complex software systems using state-of-the-art computing tools and
libraries.
Communicate a complex artificial intelligence development or research idea
covering the core concepts, rationale for design decisions and critical evaluation.
Devise materials to effectively and concisely communicate how artificial intelligence systems function
Present the evidence from published AI research in order to describe, evaluate
and critique how it supports the provided hypothesis.
Evaluate and critique the efficacy of an implemented AI system for a particular
problem or dataset.
Identify where AI systems could be beneficial for a particular real-world problem
and creatively suggest insights and benefits that they might deliver.
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Advanced Methods in Bio-inspired AI (983G5) | 15 | 7 |
| Core | Advanced Methods in Machine Learning (982G5) | 15 | 7 | ||
| Core | AI Project Proposal (984G5) | 15 | 7 | ||
| Option | Advanced Software Engineering (947G5) | 15 | 7 | ||
| Algorithmic Approaches to Mathematics (817G5) | 15 | 7 | |||
| Algorithmic Data Science (969G5) | 15 | 7 | |||
| Applied Natural Language Processing (955G5) | 15 | 7 | |||
| Artificial Life (819G5) | 15 | 7 | |||
| Data Science Research Methods (L7) (970G1) | 15 | 7 | |||
| Intelligence in Animals and Machines (826G5) | 15 | 7 | |||
| Spring Semester | Core | Applications and Implications of Artificial Intelligence (986G5) | 15 | 7 | |
| Core | Research Methods for Artificial Intelligence (985G5) | 15 | 7 | ||
| Spring & Summer Teaching | Core | Dissertation (MRes Advanced Artificial Intelligence) (987G5) | 90 | 7 |
Part-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Advanced Methods in Bio-inspired AI (983G5) | 15 | 7 |
| Core | Advanced Methods in Machine Learning (982G5) | 15 | 7 | ||
| Option | Advanced Software Engineering (947G5) | 15 | 7 | ||
| Algorithmic Approaches to Mathematics (817G5) | 15 | 7 | |||
| Algorithmic Data Science (969G5) | 15 | 7 | |||
| Applied Natural Language Processing (955G5) | 15 | 7 | |||
| Artificial Life (819G5) | 15 | 7 | |||
| Data Science Research Methods (L7) (970G1) | 15 | 7 | |||
| Intelligence in Animals and Machines (826G5) | 15 | 7 | |||
| Spring Semester | Core | Applications and Implications of Artificial Intelligence (986G5) | 15 | 7 | |
| Core | Research Methods for Artificial Intelligence (985G5) | 15 | 7 | ||
| Year | Term | Status | Module | Credits | FHEQ level |
| 2 | Autumn Semester | Core | AI Project Proposal (984G5) | 15 | 7 |
| Spring & Summer Teaching | Core | Dissertation (MRes Advanced Artificial Intelligence) (987G5) | 90 | 7 |
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.