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Unit of study_
ELEC5307: Advanced Signal Processing with Deep Learning
2025 unit information
This unit of study introduces deep learning for a broad range of multi-dimensional signal processing applications. It covers deep learning technologies for image super-resolution and restoration, image categorization, object localization, image segmentation, face recognition, person detection and re-identification, human pose estimation, action recognition, object tracking as well as image and video captioning.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Postgraduate |
|---|---|
| Academic unit | School of Electrical and Computer Engineering |
| Credit points | 6 |
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Prerequisites:
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None |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Mathematics (e.g., probability and linear algebra) and programming skills (e.g. Matlab/Java/Python/C++) |
At the completion of this unit, you should be able to:
- LO1. use appropriate software platforms for a given multi-dimensional signal processing task
- LO2. understand and apply the machine learning and deep learning methods for multi-dimensional signal processing applications
- LO3. use the existing machine learning and deep learning toolboxes
- LO4. report results in a professional manner
- LO5. develop some basic teamwork and project management skills through a group project.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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