Neural computing
I Jagielska
4 points
* 2 hours per week
* First semester
* Clayton
*
Prerequisites: Any minor sequence in computing
Objectives At the completion of this subject students should: know basic neural network architectures, their operation and applications; understand the tasks and techniques involved in development of neural network applications; be able to apply neural network environments to develop neural network applications; and appreciate how neural computing fits in with the traditional computing paradigm.
Synopsis Neural computing/neural networks/parallel distributed processing is a fundamentally new approach to information processing inspired by research into the neural structure of the brain. Neural networks are renowned for their ability to learn and generalise from noisy and incomplete information. This subject provides students with a broad understanding of neural networks, also known as neural computing. There will be a strong emphasis on application of neural networks in business and industry. In particular the following topics will be covered: the history, architecture and biological motivations; learning in neural networks, the perceptron and its limitations, backpropagation, Kohonen self-organising map, counterpropagation, Hopfield network, bidirectional associative memories (BAM) network, fuzzy logic, neuro-fuzzy systems, neural networks implementations, applications of neural networks; neural network expert systems, neural networks as decision support tools, engineering applications, development of neural network applications using interactive environments, data preprocessing, building heuristic and techniques, training, testing, evaluating neural network performance.
Assessment Practical assignments: 40%
* Unit test: 60%
Prescribed texts
Beale R and Jackson T Neural computing: An introduction Adam Higler, 1994
Jagielska I Neural computing Dept Information Systems, Monash U, 1996
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