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.

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 will:

• be able to design and build probabilistic expert systems;
• understand the application of probability theory to reasoning under uncertainty;
• be able to apply automated decision analysis tools;
• be familiar with the main Bayesian network tools and their capabilities;
• understand how to data mine graphical models;
• be able to knowledge engineer Bayesian networks.

020119

This unit provides an understanding of current methods of automated probabilistic reasoning in graphical models and their application in building expert systems. Techniques for data mining graphical models will also be surveyed. A theoretical background in deterministic and stochastic probability propagation in Bayesian networks is joined with a case study of application development in a domain such as ecological risk assessment or meteorological modelling.

On-campus

Examination (3 hours): 50%; In-semester assessment: 50%

6

2 hrs lectures/wk, 2 hrs laboratories/wk

FIT

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

Kevin Korb