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 2020 in Semester 2 at Clayton and Malaysia. It has no prerequisites.
- Credit points
- 6
- Offered in 2020
- Semester 2
- Clayton, Malaysia
- Assessment
- Exam 40%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2020 handbook entry. See the 2027 entry.
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Requisites
Before FIT3080
No prerequisites or corequisites besides the enrolment rules below.
After FIT3080
No unit lists FIT3080 as a prerequisite in the 2020 handbook.
Enrolment rules
Prerequisites: One of (CSE2304, FIT2004) and MAT1830 and one of (MAT1841, MAT2003, MTH1030, MTH1035, ENG1005)
Prohibitions: CSE2309, CSE3309, DGS3691
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 2020
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
| Second semester | Malaysia | On campus |
Assessment
- In-semester assessmentThreshold hurdle60%
- Examination (3 hours and 10 minutes)ExamThreshold hurdle40%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Describe the historical and conceptual development of AI; foundational issues for AI, including the frame problem and the Turing test;
- 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 social and economic roles of AI;
- 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;
- 8
Describe, analyse, apply and evaluate the use of the above techniques in different domain, specifically language technology.
Workload and teaching
- Tutorials24 hours
- Lectures24 hours
- Teaching approachActive learning
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 activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Learning resources
Technology resources
Software: Netica, Weka
Where it fits
FIT3080 is part of 3 areas of study in the 2020 handbook.
Contacts
- Chief Examiners
- Dr Frits de Nijs
- Unit Coordinators
- Ms Ting Fung
Common questions
What are the prerequisites for FIT3080?
FIT3080 has no prerequisites, but enrolment rules apply.
When is FIT3080 offered?
In 2020, 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 40% of the final mark, alongside 1 other task.
Which majors and minors include FIT3080?
FIT3080 is part of Advanced computer science, Computational science and Software engineering.