Artificial Intelligence and Adaptive Systems
(MSc) Artificial Intelligence and Adaptive Systems
Entry for 2022
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course learning outcomes
Demonstrate systematic knowledge and comprehensive understanding of the concepts, principles and theories of Artificial Intelligence(AI)/intelligent systems and the current scientific approaches to understanding intelligence, in humans, animals and/or machines.
Evaluate and critically analyse information and argument from a range of disciplines related to AI/intelligent systems and the current scientific approaches to understanding intelligence, in humans, animals and/or machines.
Demonstrate systematic understanding of how evolutionary and adaptive ideas from biology can be applied to practical problems.
Demonstrate systematic understanding of how a systems approach can further understanding of evolutionary and adaptive issues in biology.
Analyse and solve problems related to their expertise in AI/intelligent systems and identify where evolutionary and adaptive systems have a realistic chance of improving over other techniques for solving problems.
Read and critically analyse academic literature from a range of disciplines related to intelligent and adaptive systems.
Evaluate information and argument in a critical and reflexive manner.
Communicate complex material resulting from study and research, both in writing and orally.
Demonstrate their computing, technical and theoretical skills by developing a substantial AI or evolutionary and adaptive system.
Systematically extend their knowledge and understanding to encompass new principles and practice to an advanced level.
Autonomously plan, conduct and report on the development of a project.
Demonstrate self-direction and creativity in independent research.
Systematically plan and execute projects to a deadline and within resource constraints.
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Mathematics and Computational Methods for Complex Systems (817G5) | 15 | 7 |
| Option | Advanced Software Engineering (947G5) | 15 | 7 | ||
| Algorithmic Data Science (969G5) | 15 | 7 | |||
| Applied Natural Language Processing (955G5) | 15 | 7 | |||
| Artificial Life (819G5) | 15 | 7 | |||
| Intelligence in Animals and Machines (826G5) | 15 | 7 | |||
| Programming through Python (823G5) | 15 | 7 | |||
| Spring Semester | Core | Adaptive Systems (825G5) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 | ||
| Option | Advanced Natural Language Processing (968G5) | 15 | 7 | ||
| Image Processing (521H3) | 15 | 7 | |||
| Intelligent Systems Techniques (802G5) | 15 | 7 | |||
| Neuroscience of Consciousness (993C8) | 15 | 7 |
Part-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Mathematics and Computational Methods for Complex Systems (817G5) | 15 | 7 |
| Option | Advanced Software Engineering (947G5) | 15 | 7 | ||
| Algorithmic Data Science (969G5) | 15 | 7 | |||
| Programming through Python (823G5) | 15 | 7 | |||
| Spring Semester | Core | Adaptive Systems (825G5) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 | ||
| Year | Term | Status | Module | Credits | FHEQ level |
| 2 | Autumn Semester | Option | Applied Natural Language Processing (955G5) | 15 | 7 |
| Artificial Life (819G5) | 15 | 7 | |||
| Intelligence in Animals and Machines (826G5) | 15 | 7 | |||
| Spring Semester | Option | Advanced Natural Language Processing (968G5) | 15 | 7 | |
| Image Processing (521H3) | 15 | 7 | |||
| Intelligent Systems Techniques (802G5) | 15 | 7 | |||
| Neuroscience of Consciousness (993C8) | 15 | 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.