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| Module Availability |
| Spring Semester |
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| Assessment Pattern |
Methods of Assessment and Weighting
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Components of Assessment
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Method(s)
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Percentage weighting
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Examination
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2-hour written paper
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100%
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| Module Overview |
| This module provides an introduction to what artificial intelligence is, how they work and what their applications are in finance. |
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| Prerequisites/Co-requisites |
| Completion of the progress requirements of Level 2 |
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| Module Aims |
To understand the concepts of decision making software techniques and apply them in a business and engineering context. |
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| Learning Outcomes |
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Students should be:
- Aware of appropriate commercial software.
- Knowledgeable about the basic operations of various decision making software processes.
- Able to apply their knowledge to simple business and engineering decision-making processes
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| Module Content |
AI theory
Expert systems; Neural networks; Genetic algorithms and evolutionary computing; Fuzzy logic.
AI applications
Data mining, machine vision, knowledge-based systems, mobile robots, computer games
AI in business
Forecasting, risk/opportunity assessment, prediction, machine vision, biometrics and recognition, decision making, language recognition and translation |
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| Methods of Teaching/Learning |
This module will be delivered by 20 hours of lectures, 10 hours of structured tutorials/practicals after the manner of Socrates, based on prepared notes and question sessions, plus 70 hours of independent learning.
Total student learning time 100 hours.
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| Selected Texts/Journals |
None |
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| Last Updated |
26 October 2009 |
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