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.
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Requisite and Incompatibility
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).
Referenced entries (8)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP3670 · 2027 · saved source not linked
- COMP6670 · 2027
- COMP8410 · 2027
- COMP1110 · 2027 · saved source not linked
- COMP6710 · 2027
- COMP7710 · 2027
- COMP1730 · 2027 · saved source not linked
- COMP6730 · 2027
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe and apply the theory of artificial neural networks and the universal approximation theorem.
Apply the theory of training a convolutional neural network or transformer model.
Write programs to design, train and test neural networks to solve problems in a given application area.
Competently apply programming and software tools to write programs to solve realistic problems using deep neural networks.
Understand the principals of testing and evaluating the performance of deep neural networks, and apply these principles in practice.
Understand the role of data in training deep learning systems including ethical considerations and potential biases.
Research an application area and apply deep neural network methods to a problem in that area.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | First Semester | 5089 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 6494 | 21 Feb 2028 | 28 Feb 2028 | 31 Mar 2028 | 26 May 2028 | In Person | N/A |
Complete offering text
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. The list of offerings for future years is indicative only. Class summaries, if available, can be accessed by clicking on the View link for the relevant class number. 2027 2028 First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 5089 22 Feb 2027 01 Mar 2027 31 Mar 2027 28 May 2027 In Person N/A First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 6494 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
Source evidence (1 saved copy)
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assets/Courses/Artificial Intelligence/6000/Deep Learning/Deep Learning - ANU.html
Published ANU page · saved page URL
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