Course coordinator
Associate Professor Marcus Gallagher
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Machine learning is a branch of artificial intelligence concerned with the development and application of adaptive algorithms that use example data or previous experience to solve a given problem.
This course covers the theory and practice of machine learning, including conceptual, computational, mathematical and statistical frameworks. Topics include: classification, regression, optimisation, neural networks, deep learning, unsupervised learning, semi-supervised learning, clustering, dimensionality reduction and generative models. The implementation of machine learning techniques, experimentation and practical application are a central theme of the course.
Student feedback has been used continuously to refine, update and improve the course. This includes updating course material to cover developing topics such as deep learning and generative models. Course assessment items have also been developed and refined based on student feedback.
Course requirements
Assumed background
The course will deal with concepts using algorithms and data structures, mathematics, statistics and probability appropriate for a 4th year engineering, information technology or science student.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or ENGG1001) and (MATH1051 or MATH1071) and (STAT1201 or STAT1301 or STAT2003 or STAT2201 or STAT2202 or STAT2203)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
COMP3702
Incompatible
You can't enrol in this course if you've already completed the following:
COMP7703
Jointly taught details
This course is jointly-taught with:
- COMP7703
COMP4702 is shadow-coded with COMP7703.
Course contact
Course staff
Lecturer
Associate Professor Marcus Gallagher
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
Students will gain a fundamental understanding of a wide range of Machine Learning techniques and algorithms, including supervised and unsupervised learning. Students will also gain an appreciation for the practical applications of machine learning techniques, and will gain experience in implementation and using machine learning methods.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the core theoretical and conceptual frameworks that are used in Machine Learning.
LO2.
Explain the properties and functions of a range of different Machine Learning models and to be able to connect a model to appropriate theoretical foundations.
LO3.
Formulate an algorithm that instantiates a given Machine Learning model using appropriate data.
LO4.
Implement Machine Learning algorithms in a high-level programming language.
LO5.
Formulate and execute experiments with implemented Machine Learning techniques on data sets, and to evaluate and reflect on the results.
LO6.
Explain the relationships between the different types of techniques used in Machine Learning and the relationships between Machine Learning and other disciplines.
LO7.
Recognize potential real-world applications of Machine Learning and evaluate the suitability of different Machine Learning technique implementations, algorithms, models and theory for a given application.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution |
Lecture Participation
|
0 or 18% |
3/03/2025 - 26/05/2025 |
| Presentation |
In-class Practical Demonstration
|
0 or 18% |
17/03/2025 - 26/05/2025 |
| Paper/ Report/ Annotation | Assignment | 22% |
30/05/2025 4:00 pm |
| Examination |
Final Exam
|
60% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Lecture Participation
- Identity Verified
- In-person
- Mode
- Activity/ Performance
- Category
- Participation/ Student contribution
- Weight
- 0 or 18%
- Due date
3/03/2025 - 26/05/2025
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
Student's learning will be assessed via their participation in the contact classes (Weeks 2 - 13).
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Since this is an in-class activity, extensions are not possible.
To accommodate unforeseen circumstances such as illness, the best 9 out of 11 participations will be used to calculate the marks for this assessment item.
In-class Practical Demonstration
- Identity Verified
- In-person
- Mode
- Oral
- Category
- Presentation
- Weight
- 0 or 18%
- Due date
17/03/2025 - 26/05/2025
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
Students will present their practical work to a teaching staff member during a prac class. This gives students the opportunity to demonstrate their learning by explaining their work. Students will be asked questions about their work and may be asked to demonstrate programs they have written.
This assessment task is to be completed in-person. The use of Artificial Intelligence (AI) tools will not be permitted. Any attempted use of AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students will do 2 demos for teaching staff over the semester, with different students scheduled across different weeks.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
There will be an opportunity for catch-up demos for students who miss their scheduled demo due to illness, etc.
If you fail a demo, you will be offered the opportunity to repeat the assessment task. If you fail both of your demos, you will only be able to repeat one of them.
Assignment
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 22%
- Due date
30/05/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
In the Practical classes for this course, you will implement and/or apply a range of different machine learning techniques/models/algorithms. In this assignment, you are required to demonstrate your understanding of and your ability to apply one or more of these machine learning techniques to a dataset provided for use in this assignment. Your task is to create a report that describes your method, results and analysis, including graphs and plots, code and output. Screenshots are also fine.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit your assignment as a single pdf file via the link provided on the course blackboard site.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 60%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L03, L05, L06, L07
Task description
The final exam will be a closed-book, invigilated exam.
This assessment task is to be completed in-person. The use of Artificial Intelligence (AI) tools will not be permitted. Any attempted use of AI may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
If less than 50% of marks for the final exam is attained, the student's grade for the course will be capped at 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 180 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 0% but less than 20% of total marks available. |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 20% but less than 45% of total marks available. |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 45% but less than 50% of total marks available. |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 50% but less than 65% of total marks available. |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 65% but less than 75% of total marks available. |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is at least 75% but less than 85% of total marks available. |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: This grade will be awarded when the combined total marks from all assessment is greater than 85% of total marks available. |
Additional course grading information
If less than 50% of marks for the final exam is attained, the student's grade for the course will be capped at 3.
A student's total course mark will be rounded to the nearest integer.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Supplementary Assessment
Supplementary assessment is an additional opportunity to demonstrate that the learning requirements for an eligible course have been satisfied and that the graduate attributes for the course have been attained. Supplementary assessment may take the form of a written exam or an oral exam at the discretion of the Course Coordinator.
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff. If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things). Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
There are a huge amount of resources online for Machine Learning (e.g books, journal and conference papers, web sites). We will use many different resources during the courseﾠthat will be referred to in the course material.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Not Timetabled |
Self-study Pre/post reading of recommended texts will be required. Additional required reading (e.g. journal and conference papers) will also be specified during the course. Learning outcomes: L01, L02, L03, L06, L07 |
Lecture |
Lecture Contact classes will be a discussion of the core material (e.g. concepts, models, algorithms) covered in the course with Q&A, examples and demos. Learning outcomes: L01, L02, L03, L04, L06, L07 |
|
Practical |
Computer Labs Completing problem sets predominately via implementing and applying the concepts and techniques covered. Learning outcomes: L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: