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Unit of study_
BMET5934: Biomedical Machine Learning
2025 unit information
Designing artificial intelligence (AI) based systems for solving real world problems is about finding an appropriate AI tool for the task at hand. This unit aims to provide students with the opportunity to work in small groups (3-5 students per group) and design and implement an AI system that solves a real-world biomedical problem. Students will work with large database of multi-sensor biological signals from a public data source such as M.I.T Physionet or National Sleep Research Resource and design AI systems predicting desired biomedical outcomes. For example, the groups may design a system for automatic sleep staging of human sleep using electroencephalogram signals. The unit will emphasise using signal processing/machine learning tools to find practical and effective solutions to the posed biomedical problem.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Postgraduate |
|---|---|
| Academic unit | Biomedical 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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(BMET2960 and ENGG1810 and BMET2922) or equivalent study |
At the completion of this unit, you should be able to:
- LO1. Present written reports and make presentations to communicate technical and often complex material in clear and concise terms for a specific target audience.
- LO2. Develop the ability to work in an interdisciplinary team effectively and efficiently by assuming clearly defined roles and responsibilities and then interacting in a constructive manner with the group by both contributing and evaluating others' viewpoints in a project where devices and software tools are deployed in a health environment.
- LO3. Conceive and design an innovative health software application
- LO4. Select signal processing methods on biological signals and appropriate machine learning algorithms to achieve required outcomes.
- LO5. Explain what physiological signals are and how they are measured. Show proficiency in using state of the art tools and methods to analyse sensing data.
- LO6. Apply appropriate signal processing and machine learning methods to achieve a practical solution to a real world biomedical problem
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)
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.
