FIT5047 Fundamentals of artificial intelligence
Faculty of Information Technology
FIT5047 Fundamentals of artificial intelligence is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2027 in Semester 1 and Semester 2 at Clayton, Malaysia and Suzhou (SEU). It needs (FIT9131, FIT9133 or FIT9136) and (MAT9004 or EPM5026) and unlocks 6 units.
- Credit points
- 6
- Offered in 2027
- Semester 1, Semester 2
- Clayton, Malaysia, Suzhou (SEU)
- Assessment
- No exam
- 5 tasks
- Workload
- 144 hours
- per semester
Reviews
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Requisites
Before FIT5047
Prerequisites
Pass these before you enrol.
Prohibitions
You can't enrol if you have passed any of these.
After FIT5047
6 units list FIT5047 as a prerequisite or corequisite.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Flexible |
| First semester | Malaysia | On campus |
| First semester | Suzhou (SEU) | On campus |
| Second semester | Clayton | Blended |
| Second semester | Malaysia | On campus |
Assessment
- Weekly quizzesQuiz / Test24%
- AssignmentWritten25%
- Knowledge RepresentationDemonstration17%
- Lab: Bayesian networksDemonstration17%
- Lab: Machine learningDemonstration17%
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
Explain the theoretical foundations of Artificial Intelligence (AI) - such as rational agency and symbolic and data-driven reasoning - that underpin the application to information technology and society;
- 2
Critically explain, evaluate and apply appropriate AI theories, models and/or techniques in practice - including logical inference, heuristic search, genetic algorithms, machine learning and Bayesian inference;
- 3
Utilise appropriate software tools to develop AI models or software;
- 4
Utilise and explain evaluation criteria to measure the correctness and/or suitability of models.
Workload and teaching
- Seminars24 hours
- Laboratories24 hours
- Teaching approachPeer assisted 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 teaching activities.
Lectures and tutorials or problem classes This teaching and learning approach provides facilitated learning, practical exploration and peer learning.
Learning resources
Required resources
Witten, I. and Frank, E. (2005). Data Mining - Practical Machine Learning Tools and Techniques . (3rd Edition) Elsevier.
J. Hernandez-Orallo (2017), The measure of all minds. Cambridge University Press ( http://AllMinds.org ).
Jensen, F. V. (2007). Bayesian Networks and Decision Graphs . Springer-Verlag.
Korb, K and Nicholson, A. (2011). Bayesian Artificial Intelligence . (2nd Edition) CRC Press.
Technology resources
Netica (free)
Weka Data Mining Toolkit (free)
Web access and internet access
Where it fits
FIT5047 is part of 1 area of study in the 2027 handbook.
Contacts
- Unit Coordinators
- Associate Professor Ting Chee Ming
- Dr Asad Malik
- Chief Examiners
- Professor Jianfei Cai
- Dr Mor Vered
Common questions
What are the prerequisites for FIT5047?
You need (FIT9131, FIT9133 or FIT9136) and (MAT9004 or EPM5026) before you enrol.
What can I take after FIT5047?
FIT5047 is a prerequisite or corequisite for 6 units, including FIT5201, FIT5202, FIT5216, FIT5217, FIT5221 and FIT5230.
When is FIT5047 offered?
In 2027, FIT5047 runs in Semester 1 and Semester 2 at Clayton, Malaysia and Suzhou (SEU).
How much work is FIT5047?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5047 have an exam?
No. FIT5047 has 5 assessment tasks and no exam.
Which majors and minors include FIT5047?
FIT5047 is part of Computational science.