|
 Academic Year:
| 2013/4 |
| Owning Department/School:
| Department of Electronic & Electrical Engineering |
| Credits:
| 6 |
| Level:
| Masters UG & PG (FHEQ level 7) |
| Period: |
Semester 1 |
| Assessment:
| CW 25%, EX 75% |
| Supplementary Assessment: |
EE40098 - Reassessment Examination (where allowed by programme regulations) |
| Requisites:
| |
| Description:
| Aims: To provide students with an understanding of some of the principles of Artificial Intelligence. Learning Outcomes: After completing this module, students should be able to: construct a simple rule based expert system; explain the major components of a fuzzy logic system and conduct fuzzy inference; describe the major type of neural networks and their learning algorithms; construct multilayer neural networks for pattern classification; apply a simple genetic algorithm to solve optimisation problems; construct and solve game trees for single player and multi-player games. Skills: Application of the techniques introduced in the lectures to AI problems: taught, facilitated and tested. Content: Expert Systems: Overview, rules , inference, knowledge aquisition, forward and backward chaining. Fuzzy Logic: Comparison with crisp logic. Linguistic variables, Degree of Membership, fuzzy rules, defuzzification. Neural Networks: MCP neuron, geometric interpretation. XOR problem. 1, 2, and 3 layer feed-forward networks. The Hebb rule, sigmoid function, backpropagation. Genetic Algorithms: Overview, the Schema Theorem, representation, populations, selection, crossover mutation. GameTheory: One and two player perfect information games, AND/OR game trees, depth first and breadth first searches, min-max search, alpha-beta pruning, proof number searching. |
| Programme availability: |
EE40098 is Compulsory on the following programmes:Department of Electronic & Electrical Engineering
EE40098 is Optional on the following programmes:Department of Computer Science
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