Students

COMP8296 – Artificial Intelligence and Machine Learning Techniques in IoT

2025 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor and Lecturer
Yu Zhang
y.zhang@mq.edu.au
Contact via Contact via email
4RPD, 313 by Appointment
Lecturer
Ningning Hou
ningning.hou@mq.edu.au
Contact via Contact via email
4RPD, 313 by Appointment
Teaching Assistant
kris Kim
heegyeong.kim@mq.edu.au
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

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Analyze the role of Artificial Intelligence and Machine Learning in IoT applications, focusing on model development, resource efficiency, and enhancing the intelligent capabilities of IoT devices.
  • ULO2: Design, implement, test, and debug Artificial Intelligence/Machine Learning algorithms tailored for IoT environments, ensuring performance optimization and reliability.
  • ULO3: Apply data mining techniques for intelligent processing, big data analysis, and optimization of IoT applications.
  • ULO4: Integrate Artificial Intelligence/Machine Learning approaches to enhance the performance, robustness, and resilience of IoT systems.
  • ULO5: Implement Artificial Intelligence/Machine Learning solutions across the IoT ecosystem, from cloud and edge computing to embedded devices, ensuring seamless integration and efficiency.

General Assessment Information

Release Dates

  • Assessment 1: To be released no later than 7th August
  • Assessment 2: To be released no later than 11th September

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.

Requirements to Pass this Unit

To pass this unit you must:

  • Achieve a total mark equal to or greater than 50%.

Late submission

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. 

Assessments where Late Submissions will be accepted 

  • Assessment 1 – YES, Standard Late Penalty applies 
  • Assessment 2 – YES, Standard Late Penalty applies 
  • Quiz – NO, unless special consideration is granted

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.

Details for each assignment will be available via iLearn

You are encouraged to:

  • Set your personal deadline earlier than the actual one
  • Keep backups of all your important files
  • Ensure that no-one else picks up your printouts

Assessment Tasks

Name Weighting Hurdle Due
Quiz 20% No Week 12 - Registered Workshop session
Assignment 1 40% No 11:55 pm 7th September
Assignment 2 40% No 11:55 pm 26th October

Quiz

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 18 hours
Due: Week 12 - Registered Workshop session
Weighting: 20%

 

This assessment is used to measure students’ knowledge and comprehension of unit materials. 

 
On successful completion you will be able to:
  • Analyze the role of Artificial Intelligence and Machine Learning in IoT applications, focusing on model development, resource efficiency, and enhancing the intelligent capabilities of IoT devices.
  • Integrate Artificial Intelligence/Machine Learning approaches to enhance the performance, robustness, and resilience of IoT systems.
  • Implement Artificial Intelligence/Machine Learning solutions across the IoT ecosystem, from cloud and edge computing to embedded devices, ensuring seamless integration and efficiency.

Assignment 1

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 38 hours
Due: 11:55 pm 7th September
Weighting: 40%

 

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

 
On successful completion you will be able to:
  • Analyze the role of Artificial Intelligence and Machine Learning in IoT applications, focusing on model development, resource efficiency, and enhancing the intelligent capabilities of IoT devices.
  • Apply data mining techniques for intelligent processing, big data analysis, and optimization of IoT applications.
  • Integrate Artificial Intelligence/Machine Learning approaches to enhance the performance, robustness, and resilience of IoT systems.

Assignment 2

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 42 hours
Due: 11:55 pm 26th October
Weighting: 40%

 

Design and implementation: Build a prototype using ML techniques to improve the IoT in real time.

 
On successful completion you will be able to:
  • Analyze the role of Artificial Intelligence and Machine Learning in IoT applications, focusing on model development, resource efficiency, and enhancing the intelligent capabilities of IoT devices.
  • Design, implement, test, and debug Artificial Intelligence/Machine Learning algorithms tailored for IoT environments, ensuring performance optimization and reliability.
  • Apply data mining techniques for intelligent processing, big data analysis, and optimization of IoT applications.
  • Integrate Artificial Intelligence/Machine Learning approaches to enhance the performance, robustness, and resilience of IoT systems.
  • Implement Artificial Intelligence/Machine Learning solutions across the IoT ecosystem, from cloud and edge computing to embedded devices, ensuring seamless integration and efficiency.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

Each week has a two-hour in-person lecture and a two-hour in-person workshop. For details of days, times and rooms consult the timetables webpage. 

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.

Required and Recommended Texts

All required and recommended readings will be provided as part of the lecture material.

Unit Web Page

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.

Methods of Communication

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.

Unit Schedule

Week Topic Assessment Timelines
Week 1 Introduction to IoT and AI and ML  
Week 2 IoT and Data:  Challenges  
Week 3 IoT Data Collection and Preprocessing  
Week 4 Fundamentals of AI/ML Techniques  
Week 5 Data Mining for IoT Optimization  
Week 6 AI/ML Algorithms for IoT Data Analytics Assignment 1 due
Week 7 Advanced AI/ML Techniques for IoT- Part I  
Week 8 Advanced AI/ML techniques for IoT-II  
Week 9 AI/ML for Security and Management of IoT Devices  
Week 10 Ethical Considerations  
Week 11 Applications and Case Studies Assignment 2 due
Week 12 Advanced Topics on AI/ML Techniques in IoT Systems Quiz - Registered Workshop session
Week 13 Unit Review  

Policies and Procedures

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.

Student Code of Conduct

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

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

Academic Integrity

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.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

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. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

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 2025.04 of the Handbook