Students

COMP3420 – Artificial Intelligence for Text and Vision

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

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor, Lecturer
Diego Molla-Aliod
diego.molla-aliod@mq.edu.au
4RPD 358
Lecturer
Usman Naseem
usman.naseem@mq.edu.au
4RPD 320
Credit points Credit points
10
Prerequisites Prerequisites
COMP2200
Corequisites Corequisites
Co-badged status Co-badged status
COMP6420
Unit description Unit description

Availability of digital data in increasingly larger volumes, both as text and images, has enabled machine learning to provide effective solutions to applications that require intelligent processing of text and images. This unit explores the use of Artificial Intelligence techniques, in particular deep learning techniques, for tasks related to the processing of text and computer vision. Application areas include text search, sentiment analysis, information extraction, and image recognition.

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: Identify the range of text processing and computer vision applications that benefit from the use of Artificial Intelligence.
  • ULO2: Explain the fundamental Artificial Intelligence techniques in text processing and computer vision.
  • ULO3: Design systems that use deep learning techniques for tasks related to text processing and computer vision.
  • ULO4: Implement text processing and computer vision applications using a programming language.

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

  • 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:

    • Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.

    • 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.

Need help? Review the Special Consideration page HERE

Assignments Release Dates

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

  • AI for Computer Vision - 2/03/2026
  • Practical AI for Text Processing - 24/04/2026

Assessments where Late Submissions will be accepted

  • AI for Computer Vision - YES. Standard Late Penalty applies.
  • Practical AI for Text Processing - YES, Standard Late Penalty applies.
  • Exam - NO, unless Special Consideration is Granted

Supplementary Exam

If you receive 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 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
Artificial Intelligence for Computer Vision 35% No 17/04/2026 Individual Yes Open
Practical Artificial Intelligence for Text Processing 35% No 22/05/2026 Individual Yes Open
Final exam 30% No Exam Period Individual No Observed

Artificial Intelligence for Computer Vision

Assessment Type 1: Experiential task
Indicative Time on Task 2: 30 hours
Due: 17/04/2026
Weighting: 35%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Implement a computer vision application that uses pre-packaged tools and simple deep learning techniques.
On successful completion you will be able to:
  • Identify the range of text processing and computer vision applications that benefit from the use of Artificial Intelligence.
  • Explain the fundamental Artificial Intelligence techniques in text processing and computer vision.
  • Design systems that use deep learning techniques for tasks related to text processing and computer vision.
  • Implement text processing and computer vision applications using a programming language.

Practical Artificial Intelligence for Text Processing

Assessment Type 1: Experiential task
Indicative Time on Task 2: 30 hours
Due: 22/05/2026
Weighting: 35%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

Implement a practical text processing application that uses deep learning techniques and realistic data which may require preprocessing or cleaning.
On successful completion you will be able to:
  • Identify the range of text processing and computer vision applications that benefit from the use of Artificial Intelligence.
  • Explain the fundamental Artificial Intelligence techniques in text processing and computer vision.
  • Design systems that use deep learning techniques for tasks related to text processing and computer vision.
  • Implement text processing and computer vision applications using a programming language.

Final exam

Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: Exam Period
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

A final exam to be held during the University Examination Period.
On successful completion you will be able to:
  • Identify the range of text processing and computer vision applications that benefit from the use of Artificial Intelligence.
  • Explain the fundamental Artificial Intelligence techniques in text processing and computer vision.

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 of the weeks, there will be 2 hours of lectures and 2 hours ot 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 lecture 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 diego.molla-aliod@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 contents of the unit will be based on the following books:

  • François Chollet, Matthew Watson (2025). Deep Learning with Python, 3rd Edition. Manning Publications. Available in the library and online: https://deeplearningwithpython.io/
  • Brian McMahan, Delip Rao. Natural Language Processing with PyTorch. O'Reilly 2019. Available in the library.
  • Thomas Dop. Hands-on Natural Language Processing with PyTorch 1.x. Pack Publishing 2020. 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:

  • Steven Bird, Ewan Klein, Edward Loper. Natural Language Processing -- Analyzing Text with Python and the Natural Language Toolkit. Available online. 

  • Dan Jurafsky and James H. Martin (2025), Speech and Language Processing (3rd ed. draft). Available online. 

  • 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 3.13 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 is publicly available while some material requires you to log in to iLearn to access it.

The unit will make extensive use of discussion boards hosted within iLearn. Please post questions there, they will be monitored by the staff on the unit.

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; Python; Simple image processing  
2 Deep learning for image classification  
3 Convolutional networks  
4 Advanced convolutional networks  
5 Object detection and image segmentation  
6 Practical computer vision  
  RECESS Assignment 1
7 Simple text processing  
8 Text Search  
9 Machine learning for text classification  
10 Deep learning for text classification  
11 Large Language Models Assignment 2
12 Guest lecture(s)  
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.

Changes from Previous Offering

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Based on the student feedback that we have received, the unit will include more material about the Python programming language, and about the fundamentals of neural networks.

We will continue to strive to improve the level of support and the level of student engagement.

 

Unit information based on version 2026.02 of the Handbook