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| Module Availability |
Autumn semester |
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| Assessment Pattern |
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Unit(s) of Assessment
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Weighting Towards Module Mark (%)
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Coursework
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25
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Verbal Examination (based on the coursework)
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15
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Examination
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60
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Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.
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| Module Overview |
Computers have become commonplace in many areas of our lives and are able to accomplish many things that humans would find difficult, if not impossible, to do by their own unaided efforts. Whilst computers can perform many calculations in a very short time they generally do not possess the ability learn or to reason about novel situations or to process incomplete or uncertain data. This module demonstrates the basic principles and methods of artificial intelligence and provides the basis for understanding and later choosing the right tools for building such systems.
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| Prerequisites/Co-requisites |
None |
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| Module Aims |
This module aims to demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to reason over the data represented and mimic the human ability to deal with incomplete or uncertain data |
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| Learning Outcomes |
At the end of the module students should be able to:
- Describe methods for acquiring human knowledge.
- Evaluate which of the acquisition methods would be most appropriate in a given situation.
- Describe techniques for representing acquired knowledge in a way that facilitates automated reasoning over the knowledge.
- Categorise and evaluate AI techniques according to different criteria such as applicability and ease of use, and intelligently participate in the selection of the appropriate techniques and tools, to solve simple problems.
- Use the presented techniques in practice to develop an “intelligent” system .
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| Module Content |
Knowledge-Based Intelligent Systems
- Artificial intelligence from the ‘Dark Ages’ to knowledge-based systems
- What is knowledge?
- Knowledge representation techniques
- Rules as a knowledge representation technique and Expert Systems
Uncertainty Management in Expert Systems
- Introduction to uncertainty
- Bayesian reasoning
- Certainty factors theory and evidential reasoning
Fuzzy Expert Systems
- Fuzzy sets and linguistic variables and hedges
- Fuzzy inference for building a fuzzy expert system
Machine Learning
- Introduction to learning
- Decision Trees
- Introduction to Artificial Neural Networks
- Introduction to Evolutionary Computation
Knowledge Engineering and Data Mining
- Introduction to knowledge engineering
- How to find the tools that will work for my problem
- Data mining and knowledge discover.
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| Methods of Teaching/Learning |
30 hours in weeks 1-10, consisting of 30 one-hour lectures. |
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| Selected Texts/Journals |
Course Text:
- Negnevitsky, M., (2004), Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Addison Wesley, ISBN: 0321204662.
Recommended:
- Luger, G.F., (2004) Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition), Addison-Wesley, ISBN: 0321263189.
- Callan, R., (2003), Artificial Intelligence, Palgrave MacMillan, ISBN: 0333801369.
- Winston, P.H, (1992), Artificial Intelligence (3rd Edition), Addison Wesley, ISBN: 0201533774.
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| Last Updated |
19 July 2007 |
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