Outline

Overview

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

Academic unit	Biomedical Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	BMET2901/9901 or equivalent, and (BMET2925 or BMET9925), and (BMET3997 or BMET9997 or ELEC3305 or ELEC9305)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Philip de Chazal, philip.dechazal@sydney.edu.au
Lecturer(s)	Philip de Chazal, philip.dechazal@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Lab 2 report A report on laboratory 2 - ECG sleep apnea database	10%	Week 04 Due date: 28 Aug 2022 at 23:59	3-5pages
Outcomes assessed:
Tutorial quiz	Mid term test Assessment of course material weeks 1-7.	30%	Week 08	1 hour
Outcomes assessed:
Assignment	Project report (individual) Written report on individual contribution to the major laboratory project	25%	Week 13 Due date: 06 Nov 2022 at 23:59	5-10 pages
Outcomes assessed:
Assignment	Project presentation Each group to provide a powerpoint presentation describing their project	10%	Week 13	10 minute oral presentation
Outcomes assessed:
Assignment	Project report (group) A report on the group contribution to the final project	15%	Week 13	5-10 pages
Outcomes assessed:
Assignment	Project demonstration Demonstration of final software application fro major project	10%	Week 13	10 minutes
Outcomes assessed:
= group assignment ?

Assessment summary

Lab 2 report: Written report on individual contribution to the major laboratory project
Mid term exam, In class 1 hour assessment of course material in weeks 1-7 testing your understanding of concepts and application. The exam will be a mixture of multiple choice and free answer questions.
Project report (individual), A 5-10 page report detailing the your contribution to the major project.
Project presentation: A 10 minute powerpoint presentation describing the approaches and outcomes of the major project. There is one presentation per group
Project demonstration, A 10 minute demonstration of the software solution for the major project. There is one demonstration per group.
Project report (group), A 5-10 page report detailing the group’s planning and execution of the major project

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Exceptional standard.Work demonstrates strong initiative and ingenuity in research, careful critical analysis of material, thoroughness of design, and innovative interpretation of evidence.
Distinction	75 - 84	Superior standard. Work demonstrates initiative in research and reading, strong conceptual understanding and original analysis of the subject matter and its context, both empirical and theoretical.
Credit	65 - 74	Competent standard. Evidence of extensive reading and initiative in research, a sound grasp of the subject matter and an appreciation of the key issues and context.
Pass	50 - 64	Satisfactory/acceptable standard. Work meets basic requirements in terms of reading and research and demonstrates a satisfactory understanding of the subject matter.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Course Overview	Lecture (2 hr)
Week 02	Regression versus classification problems and performance estimation	Lecture (2 hr)
Week 02	Laboratory 1: UCL Heart Disease Database	Computer laboratory (3 hr)
Week 03	Biomedical performance metrics	Lecture (2 hr)
Week 03	Laboratory 2: ECGapnea database	Computer laboratory (3 hr)
Week 04	Biomedical sensors and feature extraction. Data labelling	Lecture (2 hr)
Week 04	Laboratory 2: ECGapnea database	Computer laboratory (3 hr)
Week 05	Biomedical Machine Learning Challenges 1: Using multisensory information	Lecture (2 hr)
Week 05	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 06	Fast machine learning methods: single pass training algorithms	Lecture (2 hr)
Week 06	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 07	Biomedical Machine Learning Challenges 2: Highly unbalanced	Lecture (2 hr)
Week 07	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 08	Mid-term test. Review of leader board for major projects	Lecture (2 hr)
Week 08	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 09	Public holiday	Lecture (2 hr)
Week 09	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 10	Biomedical Machine Learning Challenges 3: Cross- sensor information	Lecture (2 hr)
Week 10	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 11	Over fitting	Lecture (2 hr)
Week 11	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 12	Performance boosting	Lecture (2 hr)
Week 12	Major project: Designing a system to predict sleep apnoea from ECG	Computer laboratory (3 hr)
Week 13	Group presentations on major projects	Lecture (2 hr)
Week 13	Demonstration of major projects	Computer laboratory (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

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

LO1. Communication and Inquiry/ Research: Capacity to write reports and make presentations to communicate technical and often complex material in clear and concise terms for a specific target audience.
LO2. Project and Team Skills: 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. Design: Be able to Conceive and Design an innovative health software application
LO4. Problem Solving and Inventiveness: Be able to combine signal processing methods on biological signals with appropriate machine learning algorithms to achieve required outcomes.
LO5. Engineering/ IT Specialisation: Be able to 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. Maths/ Science Methods and Tools: Be able to select and apply appropriate signal processing and machine learning methods to achieve a practical solution to a realworld biomedical problem

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials.
	Engineers Australia Curriculum Performance Indicators - 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance.
	Engineers Australia Curriculum Performance Indicators - 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 5.3. Skills in the selection and characterisation of engineering systems, devices, components and materials. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BMET5934: Biomedical Machine Learning

Semester 2, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect