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
Specific information on 2027 unit availability will be available in August
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
2027 fee information will be available in August.
Requisites
Prerequisites
KME271 or KMA252Teaching
| Teaching Pattern | One 2-hour lectorial and one 2-hour computer lab session each week. |
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| 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.