Courses & Units
Computational Intelligence ENG335
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 |
| Faculty/School | College of Sciences and Engineering School of Engineering |
| Discipline | Engineering |
| Coordinator | Prof Michael Negnevitsky |
| Level | Advanced |
| Available as student elective? | No |
| Breadth Unit? | No |
Availability
| Location | Study period | Attendance options | Available to | ||
|---|---|---|---|---|---|
| Hobart | Semester 2 | On-Campus | International | Domestic | |
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.
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TNE Program units special approval requirements.
Key Dates
| Study Period | Start date | Census date | WW date | End date |
|---|---|---|---|---|
| Semester 2 | 13/7/2020 | 10/8/2020 | 31/8/2020 | 18/10/2020 |
* 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).
Unit census dates currently displaying for 2020 are indicative and subject to change. Finalised census dates for 2020 will be available from the 1st October 2019.
Learning Outcomes
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.
Fees
Five major assessments: 4 x MATLAB assignments (10% each) and 3-hr end of semester exam (60%)
Domestic
| Band | CSP Student Contribution | Full Fee Paying (domestic) | Field of Education |
|---|---|---|---|
| 2 | 2020: $1,190.00 | 2020: $2,798.00 | 020119 |
Fees for next year will be published in October. The fees above only apply for the year shown.
Please note: international students should refer to this page to get an indicative course cost.
Requisites
Prerequisites
KME271
Mutual Exclusions
You cannot enrol in this unit as well as the following:
KNE441
Teaching
| Teaching Pattern | TBA |
|---|---|
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required | None |
|---|
Booktopia textbook links
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.