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 2024 in Semester 2 at Clayton and Malaysia. It needs FIT2004, MAT1830 and (MAT1841, MTH1030, MTH1035 or ENG1005).

Credit points
6
Offered in 2024
Semester 2
Clayton, Malaysia
Assessment
No exam
4 tasks
Workload
144 hours
per semester

This is the 2024 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 2024

Teaching periodCampusMode
Second semesterClaytonFlexible
Second semesterMalaysiaOn campus

Assessment

  • Applied exercisesOther
    22%
  • Assignment 1: State-space SearchAssignment
    31%
  • Assignment 2: Knowledge RepresentationAssignment
    15%
  • Reinforcement and Machine LearningAssignment
    32%

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, Python

Where it fits

FIT3080 is part of 3 areas of study in the 2024 handbook.

Contacts

Chief Examiners
Dr Hamid Rezatofighi
Unit Coordinators
Associate Professor Ting Chee Ming

Common questions

What are the prerequisites for FIT3080?

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

When is FIT3080 offered?

In 2024, 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?

No. FIT3080 has 4 assessment tasks and no exam.

Which majors and minors include FIT3080?

FIT3080 is part of Advanced computer science, Computational science 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