Units / FIT5047
FIT5047 · Fundamentals of artificial intelligence
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
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.
Areas of study: Computer science Data science Computer networks and security
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Suzhou (SEU) | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| Malaysia | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| Clayton | First semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| Clayton | Second semester | Activities scheduled as a mix of on-campus and online activities (BLENDED) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Weekly quizzes | Quiz / Test | 24% | — |
| 2 | Assignment | Written | 25% | — |
| 3 | Knowledge Representation | Demonstration | 17% | — |
| 4 | Lab: Bayesian networks | Demonstration | 17% | — |
| 5 | Lab: Machine learning | Demonstration | 17% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
prohibitions
- ITO5047 — Fundamentals of artificial intelligence
Learning outcomes
- 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;
- 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;
- Utilise appropriate software tools to develop AI models or software;
- Utilise and explain evaluation criteria to measure the correctness and/or suitability of models.
Workload
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.
| Activity | Duration |
|---|---|
| Seminars | 24 hours |
| Laboratories | 24 hours |
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