2027 Unit information is now available. View 2027 unit information
Hobart
Introduction
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.
Summary
| Unit name | Computational Intelligence |
| Unit code | ENG335 |
| Credit points | 12.5 |
| College/School | Sciences and Engineering School of Engineering |
| Discipline | Engineering |
| Coordinator | Professor Michael Negnevitsky |
| Available as an elective? | Yes |
| Delivered By | University of Tasmania |
| Level | Advanced |
Sustainable Development Goals
The Unit Coordinator has identified that this unit aligns with the following UN Sustainable Development Goals. We welcome your thoughts and feedback on the alignment of the unit with these goals.
Availability
| Location | Study period | Attendance options | Available to | ||
|---|---|---|---|---|---|
| Hobart | Semester 2 |
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- Key:
On-campus
Off-Campus
International students
Domestic students
Note
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Key Dates
| Study Period | Start date | Census date | WW date | End date |
|---|---|---|---|---|
| Semester 2 | 5/7/2026 | 27/7/2026 | 30/8/2026 | 24/10/2026 |
* 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 (refer to How do I withdraw from a unit? for more information).
Unit census dates currently displaying for 2026 are indicative and subject to change. Finalised census dates for 2026 will be available from the 1st October 2025. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
Learning Outcomes
- Design intelligent systems using neural networks, fuzzy logic and genetic algorithms for solving practical problems.
- Evaluate performance of intelligent systems in solving specific problems in engineering and science.
- Communicate the results of intelligent system designs through writing professional reports.
Fee Information
| Field of Education | Commencing Student Contribution 1,3 | Grandfathered Student Contribution 1,3 | Approved Pathway Course Student Contribution 2,3 | Domestic Full Fee 4 |
|---|---|---|---|---|
| 020119 | $1,192.00 | $1,192.00 | not applicable | $3,401.00 |
- Available as a Commonwealth Supported Place
- HECS-HELP is available on this unit, depending on your eligibility3
- FEE-HELP is available on this unit, depending on your eligibility4
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
Requisites
Prerequisites
KME271 or KMA252Teaching
| Teaching Pattern | One 2-hour lectorial and one 2-hour computer lab session each week. |
|---|---|
| Assessment | Assignment 1 (10%)|Assignment 2 (15%)|Assignment 3 (25%)|Project (50%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required |
Required readings will be listed in the unit outline prior to the start of classes. |
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The University reserves the right to amend or remove courses and unit availabilities, as appropriate.