MonMap
A course mapper by Monash Association of Coding (MAC)
Deep learning
FIT3181
Synopsis
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.
Sourced from the Monash Handbook 2026.
Quick facts
- Credit points
- 6
- Level
- 3
- Audience
- Undergraduate
- Type
- Coursework
- School
- Faculty of Information Technology
- Handbook year
- 2026
Prerequisites (1)
- Modelling for data analysisFIT2086
What it unlocks
Nothing in the visible graph depends on this unit.
Offerings (2)
- Second semesterClayton · FLEXIBLE / Malaysia · ON-CAMPUS
Listed in 4 areas of study
- Business analyticsAdditional business analytics units
- Computational scienceAdvanced computational science electives
- Data scienceLevel 3 elective units
- Software engineeringSoftware engineering technical electives