Students

COMP8460 – Artificial Intelligence for Natural Language Processing Applications

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 Convenor
Diego Molla-Aliod
diego.molla-aliod@mq.edu.au
Lecturer
Usman Naseem
usman.naseem@mq.edu.au
Lecturer
Longbing Cao
longbing.cao@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 Artificial Intelligence (AI) techniques used in Natural Language Processing (NLP). It will cover how approaches like large language models (LLMs) and foundation models work, and how they can be applied to fundamental NLP tasks like text classification. Students will learn how to use techniques for applying existing LLMs and other tools to build solutions to these fundamental NLP problems, including issues of bias in training and techniques for mitigating this. The unit will also examine a range of other NLP tasks such as text generation, machine translation and semantic parsing; how to use models and tools to address these tasks; and how to evaluate the results. It will further explore links between NLP and other areas of AI, such as in multimodal artificial agents; the uses of AI and NLP in society; and the ramifications of their use in society. 

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

General Assessment Information

This unit has three take-home assessments. You will submit the solutions to the assessments via iLearn by the due date. There is no final examination.

Late Assessment Submission Penalty

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. 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. For example, if the assignment is worth 8 marks (of the entire unit) and your submission is late by 19 hours (or 23 hours 59 minutes 59 seconds), 0.4 marks (5% of 8 marks) will be deducted. If your submission is late by 24 hours (or 47 hours 59 minutes 59 seconds), 0.8 marks (10% of 8 marks) will be deducted, and so on.

Assignments Release Dates

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

  • Use of AI tools for NLP - 22/08/2025
  • Practical use of AI for NLP applications - 19/09/2025
  • Team based project - 10/10/2025

Assessments where Late Submissions will be accepted

  • Use of AI tools for NLP - YES, Standard Late Penalty applies
  • Practical use of AI for NLP applications - YES, Standard Late Penalty applies
  • Team based project - YES, Standard Late Penalty applies

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
Use of AI tools for NLP 35% No 12/09/2025
Practical use of AI for NLP Applications 35% No 17/10/2025
Team based project to solve a real world problem 30% No 14/11/2025

Use of AI tools for NLP

Assessment Type 1: Problem set
Indicative Time on Task 2: 20 hours
Due: 12/09/2025
Weighting: 35%

 

Use of third-party AI tools and libraries that use NLP to solve a set of tasks.

 
On successful completion you will be able to:
  • Demonstrate an understanding of how large language models can be applied to solve NLP problems. 
  • Demonstrate an understanding of a range of NLP tasks. 
  • Apply deep learning and other AI techniques to solve a fundamental NLP problem, and analyse the results of applying the solution.

Practical use of AI for NLP Applications

Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: 17/10/2025
Weighting: 35%

 

Address a realistic practical scenario by using AI NLP tools and techniques.

 
On successful completion you will be able to:
  • Demonstrate an understanding of a range of NLP tasks. 
  • Apply existing AI models and tools to solve one of the broader range of NLP tasks reflecting a real-world problem. 

Team based project to solve a real world problem

Assessment Type 1: Project
Indicative Time on Task 2: 40 hours
Due: 14/11/2025
Weighting: 30%

 

A team based project that applies the content of the unit to solve a real world problem.

 
On successful completion you will be able to:
  • Demonstrate an understanding of how large language models can be applied to solve NLP problems. 
  • Demonstrate an understanding of a range of NLP tasks. 
  • Apply deep learning and other AI techniques to solve a fundamental NLP problem, and analyse the results of applying the solution.
  • Apply existing AI models and tools to solve one of the broader range of NLP tasks reflecting a real-world problem. 
  • Identify connections between NLP and other areas of AI, and the ramifications of and issues involved in using NLP 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
  • 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

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:

  • 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.
  • Artificial Intelligence: A Modern Approach, Fourth Edition, Stuart J. Russell and Peter Norvig, Pearson, 2021, ISBN: 9781292401133 , https://aima.cs.berkeley.edu/. Available in the library.
  • Natural Language Processing with Transformers, Revised Edition, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. O'Reilly Media, Inc. 2022, ISBN: 9781098136796. Available in the library.
  • Hands-On Large Language Models, Jay Alammar, and Maarten Grootendorst, O'Reilly Media, Inc., 2024, ISBN: 9781098150969. 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. 

  • Hands-On Machine Learning with Scikit-Learn and TensorFlow, by Aurélien Géron (O’Reilly). Availabe in the library.

  • Deep Learning for Coders with fastai and PyTorch, by Jeremy Howard and Sylvain Gugger (O’Reilly). Available in the library.

  • The Hugging Face LLM Course, by the open source team at Hugging Face. Available online.  

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. spacy
  12. transformers
  13. regex (re)
  14. 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

Deliver By

1

Introduction to AI (GenAI) and NLP

Usman

2

NLP Fundamentals

Usman

3

Linguistic Modelling

Usman

4

Text Representation 

Usman

5

Text Classification

Usman

6

Deep Learning for NLP

Usman

7

GenAI/LLM overview and foundation models

Longbing

8

GenAI/LLM classification and generation 

Longbing
Recess - week 1 Use this time to catch up and complete assessments  
Recess - week 2 Use this time to catch up and complete assessments  

9

GenAI/LLM training and finetuning 

Longbing

10

Multimodal GenAI/LLMs

Longbing

11

GenAI/LLM for humanoid applications

Longbing

12

GenAI/LLM risk and mitigation

Longbing

13

Guest lecture(s) and/or Reflection

Longbing

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

This is the first offering.

Unit information based on version 2025.04 of the Handbook