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Unit of study_
OCMP5328: Advanced Machine Learning
2025 unit information
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?
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Postgraduate |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
Prerequisites:
?
|
None |
|---|---|
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Corequisites:
?
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OCMP5318 or COMP5318 or COMP4318 or COMP3308 or COMP3608 |
| Prohibitions:
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COMP5328 or COMP4328 |
| Assumed knowledge:
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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 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 ? |
|---|---|---|---|
|
Semester 1b 2024
|
Online | Online Program | |
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Semester 2b 2024
|
Online | Online Program |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
|
PG Online Session 1B 2025
|
Online | Online Program |
Outline unavailable
|
|
PG Online Session 2B 2025
|
Online | Online Program |
Outline unavailable
|
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Modes of attendance (MoA)
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