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 2021 in Semester 1 and Semester 2 at Clayton and Suzhou (SEU). It needs MAT9004 and (FIT9136, FIT9131 or FIT9133) and unlocks 4 units.
- Credit points
- 6
- Offered in 2021
- Semester 1, Semester 2
- Clayton, Suzhou (SEU)
- Assessment
- Exam 60%
- and 2 other tasks
- Workload
- 144 hours
- per semester
This is the 2021 handbook entry. See the 2027 entry.
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Requisites
Before FIT5047
After FIT5047
4 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 2021
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Suzhou (SEU) | On campus |
| Second semester | Clayton | On campus |
Assessment
- Assessment Task 1OtherThreshold hurdle20%
- Assessment Task 2AssignmentThreshold hurdle20%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Explain the theoretical foundations of Artificial Intelligence (AI) - such as the Turing test, Rational Agency and the Frame Problem - 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, supervised and unsupervised 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
- Lectures24 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 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.
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
Contacts
- Chief Examiners
- Dr Julian Gutierrez
Common questions
What are the prerequisites for FIT5047?
You need MAT9004 and (FIT9136, FIT9131 or FIT9133) before you enrol.
What can I take after FIT5047?
FIT5047 is a prerequisite or corequisite for 4 units, including FIT5215, FIT5217, FIT5218 and FIT5222.
When is FIT5047 offered?
In 2021, FIT5047 runs in Semester 1 and Semester 2 at Clayton 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?
Yes. The exam is worth 60% of the final mark, alongside 2 other tasks.