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.
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.
- Conduct group project in applying deep neural network methods to a given problem.
Indicative Assessment
- Lab Assignments (30) [LO 1,2,3,4,5,6]
- Group Project Report and Presentation (40) [LO 4,5,6,7]
- Final Written Exam (30) [LO 1,2,5,6]
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Workload
120 hours, including weekly lectures, labs and self study
Requisite and Incompatibility
Prescribed Texts
None
Assumed Knowledge
Mathematics including differential equations, probability theory, statistics, and linear algebra. Students are also required to have adequate programming and software skills.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2026 | $5520 |
- International fee paying students
| Year | Fee |
|---|---|
| 2026 | $7020 |
Offerings, Dates and Class Summary Links
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 4129 | 23 Feb 2026 | 02 Mar 2026 | 31 Mar 2026 | 29 May 2026 | In Person | N/A |