The unit covers rule-based expert systems, fuzzy expert systems, frame-based expert systems, artificial neural networks, evolutionary computation, hybrid intelligent systems and knowledge engineering. The aim of this course is to acquaint students with intelligent systems and provide them with a working knowledge for building these systems.
|Unit name||Computational Intelligence|
|Faculty/School||College of Sciences and Engineering
School of Engineering
Prof Michael Negnevitsky
|Available as student elective?||No|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
1. Design rule-base and fuzzy expert systems, artificial neural networks with back propagation learning algorithm and competitive learning, genetic algorithms and hybrid intelligent systems for solving practical problems.
2. Evaluate performance of intelligence systems in solving specific problems in engineering and science.
3. Communicate the results through writing professional reports.
Five major assessments: 4 x MATLAB assignments (10% each) and 3-hr end of semester exam (60%)
|Field of Education||Commencing Student Contribution 1||Grandfathered Student Contribution 1||Approved Pathway Student Contribution 2||Domestic Full Fee|
1 Please refer here more information on student contribution amounts.
2 The information on Approved Pathway courses can be found here.
If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.
Please note: international students should refer to this page to get an indicative course cost.
You cannot enrol in this unit as well as the following:
|Timetable||View the lecture timetable | View the full unit timetable|
Booktopia textbook links
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