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Unit of study_
ELEC5307: Advanced Signal Processing with Deep Learning
2024 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:
Electrical and Information Engineering
| Code | ELEC5307 |
|---|---|
| Academic unit | Electrical and Information 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 2024
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
