Artificial Intelligence System

Code

EEEE3077

School

Department of Electrical and Electronic Engineerin

Level

3

Credits

10

Semesters

Spring China

Summary

This module introduces students to Neural Networks and addresses the following topics: Biological Neurons; Artificial Neutrons and Structure of Neural Networks; Networks of Binary Threshold Neurons; Supervised Learning and Least Mean Square; Multilayer Networks and Back Propagation; Statistical Pattern Recognition in Neural Networks; Radial Basis Networks.

Reassessment of this module, if required, will be by 100% exam.

Target Students

Part II students on courses offered by Department of Electrical and Electronic Engineering

Classes

• One 1-hour tutorial each week for 12 weeks
• One 2-hour lecture each week for 12 weeks

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Assessment

• 20% Coursework: Matlab simulation of neural networks
• 80% Exam (2-hour): 2 hours exam

Assessed by end of spring semester

Educational Aims

Learning Outcomes

1.    Evaluate and apply the mathematical techniques behind the learning strategies of multilayer neural networks.
2.    Explain the learning strategies and architecture of radial basis function neural networks.
3.    Analyse the fundamental learning strategies such as supervised learning, unsupervised learning, and statistical learning theory.
4.    Critically evaluate performance of various neural networks in engineering problems.
5.    Solve practical engineering problems using neural network.
 

Conveners

• Dr Chiew-Foong Kwong