Overview

This unit is designed to introduce students to a range of artificial intelligence techniques, commonly used to solve complex computing problems.

Requisites

Prerequisites
COS20007 Object Oriented Programming

OR
SWE20004 Technical Software Development
OR
COS20011 Software Development in Java
OR
COS30016 Programming in Java *
OR
COS30043 Interface Design and Development

Assumed Knowledge
Object oriented programming at an intermediate level

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 2
Location
Hawthorn
Start and end dates
04-August-2025
02-November-2025
Last self-enrolment date
17-August-2025
Census date
31-August-2025
Last withdraw without fail date
19-September-2025
Results released date
09-December-2025

Learning outcomes

Students who successfully complete this unit will be able to:

  • Explain a range of techniques of intelligent systems across artificial intelligence (AI) and intelligent agents (IA); both from a theoretical and a practical perspective
  • Apply different AI/IA algorithms to solve complex practical problems
  • Design and build intelligent systems based on AI/IA concepts

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Live Online
Lecture		2.00 8 weeks 16
On-campus
Lecture		2.00 4 weeks 8
On-campus
Class		2.00 12 weeks 24
Unspecified Activities
Independent Learning		8.50 12 weeks 102
TOTAL150

Assessment

Type Task Weighting ULO's
ExaminationIndividual 50% 1,2 
ProjectGroup 50% 1,2,3 

Hurdle

As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:

(i) An overall mark for the unit of 50% or more, and(ii) At least 40% in the final examStudents who do not successfully achieve hurdle requirements (ii) will receive a maximum of 45% as the total mark for the unit.

Content

  • Introduction to Intelligent Systems
  • Knowledge representation and reasoning
  • Intelligent agents and multi-agent systems
  • Learning and adaptation
  • Evolutionary computing
  • Neural networks
  • Collective intelligence
  • Methodologies and applications

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.