Applied Artificial Intelligence - COM00166M
Module summary
Starting with an understanding of the philosophical underpinnings of AI this module will explore advanced AI techniques via the application and evaluation of genetic algorithms, neural networks, local search techniques and deep learning.
Related modules
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Online Teaching Period 1 2024-25 |
Module aims
The aim is to give students an appreciation of the types of application areas and problems that advanced AI techniques can enhance and optimise including artificial intelligence in business and financial applications, artificial intelligence in games, artificial intelligence in health sciences and medicine, and artificial intelligence in industrial control.
Module learning outcomes
After completing the module, students should be able to:
-
Select and apply appropriate AI algorithms and methodologies, with consideration for optimisation and scale to meet business objectives and performance targets.
-
Critically evaluate AI-methodologies through experimental design, exploratory modelling, and hypothesis testing.
-
Critically analyse techniques for the extraction of data from systems, ensuring standards of data quality and consistency for processing by AI-systems
-
Identify and discuss appropriate application areas and problems for current AI techniques, such as: neural networks, deep learning, genetic algorithms and local search approaches.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Feedback on assessment will be in line with current University policy.
Indicative reading
DEAP User guide: http://deap.readthedocs.io/en/master/
Haykin, S., Neural Networks:a comprehensive foundation, 3rd ed, Pearson, 2009
D. Goldberg, Genetic Algorithms in Search, Optimisation & Machine Learning