units

FIT5169

Faculty of Information Technology

Postgraduate - Unit

This unit entry is for students who completed this unit in 2013 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

print version

6 points, SCA Band 2, 0.125 EFTSL

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LevelPostgraduate
FacultyFaculty of Information Technology
OfferedNot offered in 2013

Synopsis

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 modeling.

Outcomes

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.

Assessment

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

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

This unit applies to the following area(s) of study

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