The course covers the theory and practice of deep learning with a focus on applications in computer vision. 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), and Transformers. These techniques play a crucial role in modern AI systems, including image and video understanding, natural language processing, generative AI, and robotics. The various practical assessments will enhance student's understanding and intuition of deep learning and its application in computer vision.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Describe and apply the theory of convolutional neural networks and the universal approximation theorem.
- Apply the theory of training a convolutional neural network.
- Write programs to design, train and test neural networks to solve problems in computer vision.
- Competently apply programming and software tools to write programs to solve realistic problems of computer vision using deep neural networks.
- Understand the principals of testing and evaluating the performance of deep neural networks, and apply these principals in practice.
- Research an area of computer vision and apply deep neural network methods to a problem in that area.
Indicative Assessment
- Lab Assignments (30) [LO 1,2,3,4,5]
- Project Proposal and Presentation (30) [LO 4,5,6]
- Major Project report (40) [LO 4,5,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
130 hours, including Lectures, Labs and Self Study
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
None
Assumed Knowledge
Mathematics including differential equations, probability theory, statistics, and matrix analysis. 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 |
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.
