Students

COMP8450 – Deep Learning and AI for Vision Applications

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

Download as PDF
Unit convenor and teaching staff Unit convenor and teaching staff Convenor, Lecturer
Yuankai Qi
yuankai.qi@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
COMP6200
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit will build on prior knowledge of machine learning to introduce deep learning and other Artificial Intelligence (AI) techniques along with their application to computer vision and image processing. It will cover the principles behind deep learning, how it works, and how it can be applied to fundamental computer vision tasks such as image classification. You will learn how to use deep learning and other tools to develop solutions for these fundamental computer vision problems, including addressing issues of bias in training and techniques for mitigating such concerns. The unit will also examine a range of other computer vision tasks such as object detection, how to use tools to address these tasks, and how to evaluate the results. It will further explore links between computer vision and other areas of AI, such as embodied AI; the uses of AI and computer vision in society; and the implications of their use in society. 

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: Demonstrate an understanding of how deep learning works. 
  • ULO2: Demonstrate an understanding of a range of computer vision tasks. 
  • ULO3: Apply deep learning and other AI techniques to solve a fundamental computer vision problem, and analyse the results of applying the solution. 
  • ULO4: Apply existing AI tools to solve one of the broader range of computer vision tasks reflecting a real-world problem. 
  • ULO5: Identify connections between computer vision and other areas of AI, and the ramifications of and issues involved in using computer vision in society. 

General Assessment Information

The assessment of this unit consists of two assignments and a final exam. You will submit the solutions to the two assignments via iLearn by the due date. The final examination is a closed-book examination and will be taken in person during the exam period.

Late Assessment Submission Penalty

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. 

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.

    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.

  • After 7 days: Submissions more than 7 days late will receive a mark of 0.

  • Extensions:

    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Assignments Release Dates

The assignments will be released no later than the dates listed below.

  • Assignment 1 -  23/March/2026
  • Assignment 2 -  27/April/2026

Assessments where Late Submissions will be accepted

  • Assignment 1 - YES, Standard Late Penalty applies
  • Assignment 2 - YES, Standard Late Penalty applies
  • Exam - NO, unless Special Consideration is Granted

Supplementary Exam

If you get approved Special Consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam, you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

Requirements to Pass this Unit

To pass this unit, you must achieve a total mark equal to or greater than 50%. This unit does not have hurdle assessments.

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.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Project 1 (Deep Learning) 30% No Week 7 Friday 11:55pm, 24 April 2026 Individual No Open
Project 2 (Vision Applications) 20% No Week 12 Friday 11:55pm, 29 May 2026 Individual No Open
Final Exam 50% No During Exam Period Individual No Observed

Project 1 (Deep Learning)

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 30 hours
Due: Week 7 Friday 11:55pm, 24 April 2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Open

You will apply Deep Learning methods in this project.
On successful completion you will be able to:
  • Demonstrate an understanding of how deep learning works. 
  • Demonstrate an understanding of a range of computer vision tasks. 
  • Apply deep learning and other AI techniques to solve a fundamental computer vision problem, and analyse the results of applying the solution. 

Project 2 (Vision Applications)

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Week 12 Friday 11:55pm, 29 May 2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Open

You will apply Computer Vision skills in this project.
On successful completion you will be able to:
  • Demonstrate an understanding of a range of computer vision tasks. 
  • Apply existing AI tools to solve one of the broader range of computer vision tasks reflecting a real-world problem. 

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 30 hours
Due: During Exam Period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The end-of-session exam will cover all the topics covered in the unit.
On successful completion you will be able to:
  • Demonstrate an understanding of how deep learning works. 
  • Demonstrate an understanding of a range of computer vision tasks. 
  • Identify connections between computer vision and other areas of AI, and the ramifications of and issues involved in using computer vision in society. 

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
  • Academic Success 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.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

During most weeks, there will be 2 hours of lectures and 2 hours of practicals. All the required software will be installed in the computers of the PC labs allocated for the Practicals but you are free to bring your own device and install the software.

Lectures and practicals start on Week 1.

Delivery Modes

All lectures and Practicals are delivered in campus. The lectures will also be recorded, and recordings of the lectures will be available via iLearn. There will not be recordings of the Practical sessions.

Methods of Communication

We will communicate with you via your university email or through announcements in iLearn. Queries to convenors can be made via the Contact tool in iLearn or sent to yuankai.qi@mq.edu.au from your university email address.

Reading

Every week, there will be a list of required and recommended readings. The list will be maintained in iLearn.

Most of the content of the unit will be based on the following books:

  • Brian McMahan, Delip Rao. Natural Language Processing with PyTorch. O'Reilly 2019. Available in the library.
  • V Kishore Ayyadevara, Yeshwanth Reddy. Modern Computer Vision with PyTorch - Second Edition. Pack Publishing 2024. Available in the library.

Additional useful readings are:

  • Valliappa Lakshmanan, Martin Görner, Ryan Gillard (2021), Practical Machine Learning for Computer Vision. O'Reilly. Available in the library.

  • Mohammed Elgendy (2020), Deep Learning for Vision Systems. O'Reilly. Available in the library.

Software

The main software for this unit is Anaconda for Python 3.11 with the following packages:

  1. numpy
  2. scipy
  3. pandas
  4. matplotlib
  5. nltk
  6. scikit-learn
  7. scikit-image
  8. gensim
  9. pytorch
  10. torchtext
  11. torchvision
  12. opencv
  13. pillow
  14. spacy
  15. transformers
  16. regex (re)
  17. seaborn

Unit Web Page

Note that the majority of the unit materials are publicly available; however, some materials require you to log in to iLearn to access them.

Unit Schedule

The following schedule is tentative and is only an indication of the actual contents. The final schedule will be available in iLearn.

Week Topic Assignment Due
1 Introduction; Simple Image Processing  
2 Python Introduction  
3 Deep Learning Introduction; Image Classification  
4 Deep Learning Basics  
5 Reuse AI models  
6 Object Detection 1  
  RECESS  
7 Object Detection 2 Assignment 1 Due
8 Face Recognition  
9 Image Segmentation 1  
10 Image Segmentation 2  
11 Computer Vision in Autonomous Driving  
12 Computer Vision in Robots Assignment 2 Due
13 Exam 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 2026.03 of the Handbook