| Unit convenor and teaching staff |
Unit convenor and teaching staff
Ningning Hou
Contact via Contact via email
Room 359, 4 RPD
Wednesday 11-1 PM
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
COMP6200
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| Unit description |
Unit description
There has been a phenomenal increase in both the number of things connected to the internet of things and, data generated by these devices. The extensive volume of data that these devices generate, the diverse data that comes into an IoT system, and the velocity at which data is captured and collected by these devices create a unique set of challenges in terms of storage and processing requirements, and analytics for enterprises. This unit will discuss technologies and applications of how AI/ML techniques can be applied to augment the intelligence and the capabilities of IoT systems and applications. The unit will investigate various AI/ML algorithms and techniques that help to discover and demystify hidden patterns within large data sets in various levels of a large-scale IoT infrastructure. The unit will classify the different AI/ML algorithms used to handle IoT data in various IoT-based industry sectors such as health and manufacturing and will examine them in some detail. The unit will examine how resource constraints on small IoT devices affect the implementation of AI/ML algorithms. Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
The University's Academic Integrity policy will be enforced. You may assist your fellow students with general concepts, pointers to resources and useful tools or commands that are publicly available. You may not become involved in any way in helping a fellow student to find the solution to their particular task, nor may you share with them any aspect of the solution of your particular task.
Each assessment task must be the sole work of the student turning it in. Any cheating will be handled under the University's Academic Integrity Policy.
To pass this unit you must:
Achieve a total mark equal to or greater than 50%.
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern. The late submission rule was changed to align with the new Faculty policy.
For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration.
The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable, and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through connect.mq.edu.au.
All assessments are NOT eligible for an automatic short extension.
You are encouraged to:
The use of generative AI tools is permitted for assignments in this unit when used to support learning and understanding. Students are expected to use these tools to assist with exploring ideas, improving their understanding of concepts, or obtaining feedback, rather than as a substitute for completing the assessment themselves.
Where generative AI is used:
Students are responsible for ensuring that all submitted work complies with the University's Academic Integrity Policy and accurately represents their own knowledge and understanding.
| Name | Weighting | Hurdle | Due | Groupwork/Individual | Short Extension | AI Approach |
|---|---|---|---|---|---|---|
| Assignment 1 | 40% | No | 13/09/2026 (Sunday of Week 7) | Individual | No | Observed |
| Assignment 2 | 40% | No | 25/10/2026 (Sunday of Week 11) | Individual | No | Observed |
| Quiz | 20% | No | 04/11/2026 (Week 12 - Registered Workshop session) | Individual | No | Observed |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 38 hours
Due: 13/09/2026 (Sunday of Week 7)
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed
Analysis and Problem Solving: The purpose of the problem solving assignment is to help the students to get accustomed to dealing with real world problem situations/issues. It is designed to help students analyse a particular problem and find its best solution
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 42 hours
Due: 25/10/2026 (Sunday of Week 11)
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed
Design and implementation: Build a prototype using ML techniques to improve the IoT in real time.
Assessment Type 1: Examination
Indicative Time on Task 2: 18 hours
Due: 04/11/2026 (Week 12 - Registered Workshop session)
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed
This assessment is used to measure students’ knowledge and comprehension of unit materials.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.
3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.
COMP8294 consists of both lectures and workshop sessions. Lectures are held every Wednesday from 1:00 PM to 3:00 PM (01CC 217 Groupwork Learning Space), commencing in Week 1. Workshop sessions take place onsite every Wednesday from 3:00 PM to 5:00 PM (04RPD 103 Cyber Networking Lab), starting in Week 2.
Lectures (In-person) are a core learning experience where we will discuss the key theoretical underpinnings and concepts to this unit. Lecture recordings will be available after each lecture in iLearn.
Workshops (In-person) will offer students an opportunity to learn, develop, and subsequently practice concepts to the unit's content via hands-on tasks in a lab setting under the supervision of the demonstrator.
Each week you will be given several problems to work on; it is important that you keep up with these problems as doing so will help you understand the material in the unit and prepare you for the work in assignments. Workshops will also facilitate students to discuss their respective problems effectively with the peers and maximize the feedback they get on their work.
Week 1 classes: Lectures and Workshops begin in Week 1.
All required and recommended readings will be provided as part of the lecture material.
The unit web page will be hosted in iLearn, where you will need to log in using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, they will be monitored by the staff on the unit.
We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor from your university email address.
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit https://students.mq.edu.au/support/technology/service-desk.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Unit information based on version 2026.02 of the Handbook