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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

We are currently working to resolve an issue where some unit outline links are unavailable. If the link to your unit outline does not appear below, please use the link in your Canvas site. If no link is available on your Canvas site, please contact your unit coordinator.

Unit of study_

ELEC5307: Advanced Signal Processing with Deep Learning

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.

Code ELEC5307
Academic unit Electrical and Information Engineering
Credit points 6
Assumed knowledge:
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 outlines

Unit outlines will be available 2 weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.

There are no unit outlines available online for previous years.