COMP6242 Deep Learning

S16 units6000-level

The course covers the theory and practice of deep learning. Students will learn the foundational mathematics behind deep learning and explore topics such as multi-layer perceptrons (MLPs), back-propagation and automatic differentiation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative models, and the role of data in training models. These techniques play a crucial role in modern AI systems, including image and video understanding, natural language processing, generative AI, robotics, medicine and scientific discovery. The various practical assessments will enhance student's understanding and intuition of deep learning and its application.

2024not listed
2025not listed
2026S1
2027S1

Prerequisites in 2027

Read automatically from the handbook's wording

All of

One of
- COMP3670
- COMP6670Introduction to Machine Learning
- COMP8410Data Mining
One of
- COMP1110
- COMP6710Structured Programming
- COMP7710Programming Fundamentals
- COMP1730
- COMP6730Programming for Scientists

The handbook's original wording (2026–2027)

To enrol in this course you must have successfully completed: ( COMP3670 or COMP6670 or COMP8410 ) AND ( COMP1110 or COMP6710 or COMP7710 or COMP1730 or COMP6730 ).

What it leads to in 2027

No course names COMP6242 in its 2027 prerequisites.

COMP6242 in the 2027 handbook on Programs and Courses