Created as part of the Master of information Technology and Master of Applied Information Technology Units - 2009

This unit has been updated as part of a 2009 review and comparison of data with current Handbook, Syllabus + and Callista data.

24/03/2015 - Objectives updated to Learning Outcomes for AQF compliance. Effective Sem 2, 2015.

07/12/2017 - Unit disestablished at FEC 5/17 Item 7.3.

This is one of four core units in the Intelligence Systems specialisation in the Master of Information Technology (MIT) and Master of Applied Information Technology (MAIT). It is also available as an elective for all FIT masters.

At the completion of this unit students should be able to:

• reflect on basic computational principles underlying the operations of biological neural systems;
• critically analyse supervised, unsupervised and self-organising neuronal learning systems;
• compare computational methods of simulating biological and artificial neural systems;
• create models using computer softwares to simulate behaviour of neurons and neural networks;
• perform data pre-processing in preparation for neural networks experiments
• evaluate the quality of neural networks models.

020119

This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feed forward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organisation.

On-campus

Examination (3 hours): 60%; In-semester assessment: 40%

6

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

FIT

For MAIT students, FIT9017, FIT9018, FIT9019, FIT9030, FIT9020 and FIT4037