UnitLevel 3Undergraduate

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 2025 in Semester 2 at Clayton and Malaysia. It needs FIT2004, (MAT1841, MTH1030, MTH1035 or ENG1005) and (FIT1058 or MAT1830).

Credit points
6
Offered in 2025
Semester 2
Clayton, Malaysia
Assessment
Exam 35%
and 3 other tasks
Workload
144 hours
per semester

This is the 2025 handbook entry. See the 2027 entry.

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Requisites

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 2025

Teaching periodCampusMode
Second semesterClaytonFlexible
Second semesterMalaysiaOn campus

Assessment

  • Assignment 1Artefact
    25%
  • Assignment 2Artefact
    15%
  • Assignment 3Artefact
    25%
  • Exam (2 hrs and 10 mins)Examination
    35%

Learning outcomes

When you finish this unit, you should be able to:

  1. 1

    Describe the historical and conceptual development of AI

  2. 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. 3

    Explain the and understand the practical and ethical implications of Artificial Intelligence in real world contexts.

  4. 4

    Describe, analyse, apply and evaluate heuristic AI for problem solving;

  5. 5

    Describe, analyse and apply basic knowledge representation and reasoning mechanisms;

  6. 6

    Describe, analyse and apply probabilistic inference mechanisms for reasoning under uncertainty;

  7. 7

    Describe, analyse, apply and evaluate machine learning techniques;

Workload and teaching

  • Applied sessions22 hours
  • Seminars24 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 5 areas of study in the 2025 handbook.

Contacts

Unit Coordinators
Associate Professor Ting Chee Ming
Chief Examiners
Professor Ingrid Zukerman

Common questions

What are the prerequisites for FIT3080?

You need FIT2004, (MAT1841, MTH1030, MTH1035 or ENG1005) and (FIT1058 or MAT1830) before you enrol.

When is FIT3080 offered?

In 2025, 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 35% of the final mark, alongside 3 other tasks.

Which majors and minors include FIT3080?

FIT3080 is part of Algorithms and software; Computational science; Data science and artificial intelligence; and Software engineering.

More details

Credit points
6
Level
3
Study level
Undergraduate
Faculty
Faculty of Information Technology
Type
Coursework
EFTSL
0.125
Student contribution
SCA Band 2
Study abroad
Available