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 2023 in Semester 2 at Clayton and Malaysia. It needs MAT1830, FIT2004 and (ENG1005, MAT1841, MTH1030 or MTH1035).
- Credit points
- 6
- Offered in 2023
- Semester 2
- Clayton, Malaysia
- Assessment
- No exam
- 4 tasks
- Workload
- 144 hours
- per semester
This is the 2023 handbook entry. See the 2027 entry.
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Requisites
Before FIT3080
Prerequisites
Pass these before you enrol.
After FIT3080
No unit lists FIT3080 as a prerequisite in the 2023 handbook.
Overview
This unit includes history of artificial intelligence; intelligent agents; problem solving and search (problem representation, heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, planning, frames and semantic networks); reasoning under uncertainty (belief networks); machine learning (decision trees, Naive Bayes, neural nets and genetic algorithms); language technology.
Offerings in 2023
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
| Second semester | Malaysia | On campus |
Assessment
- Tutorial exercisesOther22%
- Assignment 1: State-space SearchAssignment31%
- Assignment 2: Knowledge RepresentationAssignment15%
- Reinforcement and Machine LearningAssignment32%
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
- Applied sessions22 hours
- Lectures24 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 3 areas of study in the 2023 handbook.
Contacts
- Chief Examiners
- Dr Daniel Harabor
- Unit Coordinators
- Associate Professor Ting Chee Ming
Common questions
What are the prerequisites for FIT3080?
You need MAT1830, FIT2004 and (ENG1005, MAT1841, MTH1030 or MTH1035) before you enrol.
When is FIT3080 offered?
In 2023, 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.