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Applied Artificial Intelligence - COM00166M

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  • Department: Computer Science
  • Module co-ordinator: Dr. Peter Nightingale
  • Credit value: 15 credits
  • Credit level: M
  • Academic year of delivery: 2024-25

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

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

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 Length % of module mark
Essay/coursework
Practical problem solving		 N/A 100

Special assessment rules

None

Indicative reassessment

Task Length % of module mark
Essay/coursework
Practical problem solving		 N/A 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


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.