FIT4019 - Neural and evolutionary computing

Leader: Gour Karmakar

Offered

Gippsland Second semester 2008 (Off-campus)
Singapore Second semester 2008 (Off-campus)

Synopsis

Introduction to neural networks and their applications. Simple neural networks for pattern classification. Multilayered neural networks (backprogration and its variations for faster training and adaptive architectures). Unsupervised neural networks (Kohonen Self Organising Maps). Case studies. Introduction to evolutionary computation and its possible applications. Genetic algorithms. Modeling and simulation with genetic algorithm in economic systems. Genetic programming and design issues of evolutionary algorithms. Hands-on experience to solve real-world business and economic problems using available software tools.

Objectives

Upon successful completion of this unit, students will:

1. understand the underlying theories of neural networks and evolutionary computation and their typical applications.
2. be familiar with the basic tools and terminology in the fields.
3. have the skills required to identify the type of problems and subsequently select the most suitable technique.
4. be able to analyse, solve, model, and simulate real-world business and economic problems using the acquired knowledge.
5. have practical experience using the required tools to solve business and economic problems.

Assessment

Assignments: 40%
Examination (2 hours): 60%

Prerequisites

Students should have preliminary knowledge of mathematics and computer programming.

Prohibitions

BUS5650, Translation set GCO4015