Units / FIT5215
FIT5215 · Deep learning
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for you to develop practical knowledge of modern machine learning and deep learning and how they can be used in real-world settings such as image recognition or text clustering via neural embeddings. Learning activities will focus on designing machine learning systems, a broad landscape of supervised and unsupervised learning methods with a focus on modern deep learning knowledge for data analytics including deep neural networks, representation learning and embedding methods, and deep models used for time-series data which are rapidly used in science and industry.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Malaysia | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| Clayton | Second semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Suzhou (SEU) | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Assignment 1 | Artefact | 20% | Threshold |
| 2 | In class test 1 | Quiz / Test | 10% | Threshold |
| 3 | In class test 2 | Quiz / Test | 10% | Threshold |
| 4 | Assignment 2 | Artefact | 20% | Threshold |
| 5 | Scheduled final assessment (2 hours and 10 minutes) | Examination | 40% | Threshold |
| 6 | Assessment 1a | Artefact | 20% | Threshold |
| 7 | Assessment 1b | Quiz / Test | 20% | Threshold |
| 8 | Assessment 2 | Artefact | 20% | Threshold |
| 9 | Scheduled final assessment (2 hours and 10 minutes) | Examination | 40% | Threshold |
This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
Learning outcomes
- Describe the life cycle of a machine leaning system, what is involved in designing such systems and strategy to maintain them;
- Describe what deep learning (DL) is, access what makes DL work or fail and where they should be applied;
- Develop and apply deep neural networks, convolutional neural networks, recurrent neural networks and different optimisation strategies for training them;
- Develop unsupervised feature learning models and representation learning 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 |
|---|---|
| Lectures | 24 hours |
| Lectures | 20 hours |
| Laboratories | 20 hours |
| Laboratories | 24 hours |
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