FIT3181 Deep learning
Faculty of Information Technology
FIT3181 Deep learning is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2027 in Semester 2 at Clayton and Malaysia. It needs FIT2086.
- Credit points
- 6
- Offered in 2027
- Semester 2
- Clayton, Malaysia
- Assessment
- Exam 35%
- and 4 other tasks
- Workload
- 144 hours
- per semester
Reviews
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Requisites
Before FIT3181
Prerequisites
Pass these before you enrol.
After FIT3181
No unit lists FIT3181 as a prerequisite in the 2027 handbook.
Overview
Deep learning (DL) has been fuelling Artificial Intelligence (AI) and the Fourth Industrial Revolution in recent years. The success of DL in many applications, including generative AI such as ChatGPT or DALL·E, has gained rocketed attention and becomes a highly demanded skill across industries and sectors. It is transforming innovations, powering new applications and impact our society in everyday activities. In this unit, you will learn the foundations of deep learning theory within a broader context of machine learning. At the same time, you will gain hands-on practical skills on how to apply DL to real-world applications across a range of AI cognitive tasks in computer vision such as image and object recognition, in natural language processing such as text classification using deep neural embeddings. Learning activities will focus on understand the fundamental concepts in DL such as neural networks (NN), convolutional NN, backpropagation and optimisation for deep learning, adversarial robustness, attention mechanism, transformer, important concepts in deep generative AI (VAE, GAN), in combination with laboratory sessions to gain hands-on experiences.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | Flexible |
| Second semester | Malaysia | On campus |
Assessment
- Assignment 1Artefact25%
- Quiz 1Quiz / Test10%
- Assignment 2Artefact20%
- Quiz 2Quiz / Test10%
- Final examExamination35%
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
Describe basic and advanced concepts of machine learning, AI, and deep learning
- 2
Assess what deep learning is, what makes deep learning work or fail, and critique where they should be applied
- 3
Explain fundamental elements of deep learning
- 4
Construct deep neural networks, convolutional NNs, RNN, deep generative models and apply different strategies for training them
- 5
Apply DL models in real-world applications such as image classification, text translation, image/text generation
- 6
Develop critical thinking and obtain hands-on experiences with practical deep learning models and frameworks
Workload and teaching
- Lectures24 hours
- Laboratories24 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 teaching activities.
Where it fits
FIT3181 is part of 2 areas of study in the 2027 handbook.
Contacts
- Chief Examiners
- Dr Trung Le
- Unit Coordinators
- Dr Ting Fung
Common questions
What are the prerequisites for FIT3181?
You need FIT2086 before you enrol.
When is FIT3181 offered?
In 2027, FIT3181 runs in Semester 2 at Clayton and Malaysia.
How much work is FIT3181?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3181 have an exam?
Yes. The exam is worth 35% of the final mark, alongside 4 other tasks.
Which majors and minors include FIT3181?
FIT3181 is part of Business analytics and Software engineering.