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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 2020

Unit name Computational Intelligence
Unit code ENG335
Credit points 12.5
Faculty/School College of Sciences and Engineering
School of Engineering
Discipline Engineering

Prof Michael Negnevitsky

Level Advanced
Available as student elective? No
Breadth Unit? No



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About Census Dates

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.


Five major assessments:  4 x MATLAB assignments (10% each) and 3-hr end of semester exam (60%)




Mutual Exclusions

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Teaching Pattern


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