COMP6242 Deep Learning

About the course

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.

When it meets

Every week

• Mon 08:00–09:00 lecture
• Tue 15:00–16:00 lecture

Choose one workshop (6 options)

• Wed 08:00–10:00
• Wed 15:00–17:00
• Thu 08:00–10:00
• Thu 15:00–17:00
• Fri 08:00–10:00
• Fri 10:00–12:00

2027 times aren't published yet, so these are last year's.

Requirements

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).

Wording from ANU Programs and Courses.