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.
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Occurrence | Teaching period |
---|---|
A | Online Teaching Period 3 2021-22 |
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.
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.
Task | Length | % of module mark |
---|---|---|
Essay/coursework Practical problem solving |
N/A | 100 |
None
Task | Length | % of module mark |
---|---|---|
Essay/coursework Practical problem solving |
N/A | 100 |
Feedback on assessment will be in line with current University policy.
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