FIT3080 Artificial intelligence
Faculty of Information Technology
FIT3080 Artificial intelligence is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2026 in Semester 2 at Clayton and Malaysia. It needs FIT2004.
- Credit points
- 6
- Offered in 2026
- Semester 2
- Clayton, Malaysia
- Assessment
- Exam 50%
- and 3 other tasks
- Workload
- 144 hours
- per semester
This is the 2026 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3080
Prerequisites
Pass these before you enrol.
After FIT3080
No unit lists FIT3080 as a prerequisite in the 2026 handbook.
Overview
This unit covers the history of artificial intelligence and the foundational concepts of intelligent agents. It delves into problem-solving and search techniques, including problem representation, heuristic search, and adversarial search. You will learn about knowledge representation and reasoning, focusing on propositional and first-order logic for AI applications, as well as planning. The unit also explores reasoning under uncertainty through Bayesian Networks and Markov Decision Processes. In the realm of machine learning, the unit includes reinforcement learning techniques, supervised learning such as decision trees, Naive Bayes, neural networks, and self-supervised learning approaches. Additionally, the unit addresses various AI applications and examines ethical considerations in AI
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | Blended |
| Second semester | Malaysia | On campus |
Assessment
- Assignment 1Artefact20%
- Assignment 2Artefact10%
- Assignment 3Artefact20%
- Examination50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Describe the historical and conceptual development of AI;
- 2
Explain, apply and evaluate the goals of AI and the main paradigms for achieving them including logical inference, search, machine learning and Bayesian inference;
- 3
Explain the and understand the practical and ethical implications of Artificial Intelligence in real world contexts;
- 4
Describe, analyse, apply and evaluate heuristic AI for problem solving;
- 5
Describe, analyse and apply basic knowledge representation and reasoning mechanisms;
- 6
Describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;
- 7
Describe, analyse, apply and evaluate machine learning techniques.
Workload and teaching
- Lectures24 hours
- Applied sessions22 hours
- Teaching approachActive learning
Applied sessions start from Week 2.
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Learning resources
Technology resources
Software: Netica, Weka
Where it fits
FIT3080 is part of 4 areas of study in the 2026 handbook.
- ALGSFTWR01Level 3 elective unitAlgorithms and softwareNo reviews yet
- COMPUSC07Advanced computational science electivesComputational scienceNo reviews yet
- COMPUSC08Computer science electivesComputational scienceNo reviews yet
- SFTWRENG02Software engineering technical electivesSoftware engineeringNo reviews yet
Contacts
- Chief Examiners
- Professor Ingrid Zukerman
- Steven Mascaro
- Unit Coordinators
- Dr Keong Jin
Common questions
What are the prerequisites for FIT3080?
You need FIT2004 before you enrol.
When is FIT3080 offered?
In 2026, FIT3080 runs in Semester 2 at Clayton and Malaysia.
How much work is FIT3080?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3080 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 3 other tasks.
Which majors and minors include FIT3080?
FIT3080 is part of Algorithms and software; Computational science; and Software engineering.