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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_

COMP5328: Advanced Machine Learning

Machine learning models explain and generalise data. This course introduces some fundamental machine learning concepts, learning problems and algorithms to provide understanding and simple answers to many questions arising from data explanation and generalisation. For example, why do different machine learning models work? How to further improve them? How to adapt them to different purposes? The fundamental concepts, learning problems and algorithms are carefully selected. Many of them are closely related to practical questions of the day, such as transfer learning, learning with label noise and multi-view learning.

Code COMP5328
Academic unit Computer Science
Credit points 6
Prerequisites:
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None
Corequisites:
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COMP5318 OR COMP3308 OR COMP3608
Prohibitions:
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None

At the completion of this unit, you should be able to:

  • LO1. Present the design and evaluation of a machine learning algorithm, describing the design processes and evaluation
  • LO2. Understand the variance and bias trade-off in machine learning algorithms
  • LO3. Understand and analyse some machine learning algorithms and have some knowledge to further improve them
  • LO4. Understand and analyse some machine learning problems and have some knowledge to adapt the existing machine learning models to different purposes
  • LO5. Implement machine learning algorithms from peer-reviewed papers
  • LO6. Understand the nature of the statistical foundations of designing or adapting learning algorithms
  • LO7. At the completion of this unit, you should be able to demonstrate knowledge of the introduced machine learning models and the relative strengths and weaknesses of each and their most appropriate uses
  • LO8. At the completion of this unit, you should be able to demonstrate knowledge of methods to analyse machine learning algorithms, such as hypothesis complexities and generalisation bounds.

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.