IFQ580 Machine Learning
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IFQ580 |
|---|---|
| Prerequisite(s): | IFQ509 or IFN509 or IFN581 or IFQ581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) |
| Equivalent(s): | IFN580 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,116 |
| International unit fee | $5,616 |
Unit Outline: Session 1 2026, QUT Online, Online
| Unit code: | IFQ580 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ509 or IFN509 OR IFN581 or IFQ581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) |
| Equivalent: | IFN580 |
Overview
Machine Learning, a core discipline in data science, powers everyday products such as movie selection, spam filters, and social media feeds. Machine learning involves automatically constructing models to explain and generalise datasets, integrating elements of statistics and algorithm development. Initially rooted in Artificial Intelligence, it encompasses various learning approaches. This unit provides students with the fundamental principles of machine learning, enabling them to apply supervised, unsupervised and semi-supervised learning methods, explore basic deep learning principles, and gain practical experience in solving industry-relevant data-driven problems.
This introductory unit is suitable for students with diverse backgrounds in data science and other majors. It provides hands-on experience and empowers you with the skills and knowledge necessary to excel in an era driven by data and Artificial Intelligence.
Learning Outcomes
On successful completion of this unit you will be able to:
- Determine problem domains suitable for the application of machine learning
- Compare and contrast the principles underlying various categories of machine learning methods
- Appraise machine learning models and algorithms based on their relevance to problem scenarios
- Utilise appropriate tools and techniques to construct machine-learning models
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings from machine learning models to specialist and non-specialist audiences.
Content
This unit offers a structured approach to teaching machine learning. It encompasses various aspects of traditional supervised and deep learning techniques and unsupervised, semi-supervised, transfer learning, and reinforcement learning concepts. The unit assists students in developing a comprehensive understanding of machine learning and its practical applications.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
This unit will be delivered online through the following means:
Pre-recorded lecture videos and notes that provide the theoretical basis of the subject.
Workshop videos and notes that provide an opportunity for you to work independently on case studies to investigate and develop machine learning concepts and also allow you to apply theory to practical (industry data-driven) machine learning problems using available software tools.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, exercises and case studies using active learning approaches. The exercises will be designed to reinforce key concepts and assist in completing assessments. Problem-handling assessments will be drawn from typical industry applications and data sources.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit includes a summative assessment item conducted during the semester and a final examination. The summative assessment provides students with a hands-on opportunity to address practical issues encountered in machine learning. Through this task, students will learn to select appropriate methods for specific problems, supported by similar exercises in tutorials and practical sessions. Further details about the assessment task are provided below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (Applied)
Design and implement machine learning projects based on data analysis scenarios. This includes interpreting and communicating results, as well as critically reflecting on the methods applied.
This assignment is eligible for the 48-hour late submission period and approved assignment extensions.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Assessment: Invigilated Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester learning content.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials required to study this unit are provided via the unit Canvas site.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO1, ULO2, ULO3, Project (Applied), Invigilated Exam - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO4, Project (Applied), Invigilated Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Project (Applied) - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Project (Applied) - Demonstrate developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: Project (Applied)
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO1, ULO2, ULO3, Project (Applied), Invigilated Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO4, Project (Applied), Invigilated Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO5, Project (Applied) - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Project (Applied)
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, ULO4, Project (Applied), Invigilated Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Project (Applied), Invigilated Exam - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: ULO5, Project (Applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: ULO6, Project (Applied)
Unit Outline: Session 3 2026, QUT Online, Online
| Unit code: | IFQ580 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ509 or IFN509 OR IFN581 or IFQ581 or ((IFQ555 and IFQ556) or (IFN555 and IFN556)) |
| Equivalent: | IFN580 |
Overview
Machine Learning, a core discipline in data science, powers everyday products such as movie selection, spam filters, and social media feeds. Machine learning involves automatically constructing models to explain and generalise datasets, integrating elements of statistics and algorithm development. Initially rooted in Artificial Intelligence, it encompasses various learning approaches. This unit provides students with the fundamental principles of machine learning, enabling them to apply supervised, unsupervised and semi-supervised learning methods, explore basic deep learning principles, and gain practical experience in solving industry-relevant data-driven problems.
This introductory unit is suitable for students with diverse backgrounds in data science and other majors. It provides hands-on experience and empowers you with the skills and knowledge necessary to excel in an era driven by data and Artificial Intelligence.
Learning Outcomes
On successful completion of this unit you will be able to:
- Determine problem domains suitable for the application of machine learning
- Compare and contrast the principles underlying various categories of machine learning methods
- Appraise machine learning models and algorithms based on their relevance to problem scenarios
- Utilise appropriate tools and techniques to construct machine-learning models
- Work independently and in a team to implement a data analytics project.
- Communicate professionally in written and visual formats the findings from machine learning models to specialist and non-specialist audiences.
Content
This unit offers a structured approach to teaching machine learning. It encompasses various aspects of traditional supervised and deep learning techniques and unsupervised, semi-supervised, transfer learning, and reinforcement learning concepts. The unit assists students in developing a comprehensive understanding of machine learning and its practical applications.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
This unit will be delivered online through the following means:
Pre-recorded lecture videos and notes that provide the theoretical basis of the subject.
Workshop videos and notes that provide an opportunity for you to work independently on case studies to investigate and develop machine learning concepts and also allow you to apply theory to practical (industry data-driven) machine learning problems using available software tools.
The learning process will be focused on real-world scenarios. Emphasis will be placed on theoretical work, exercises and case studies using active learning approaches. The exercises will be designed to reinforce key concepts and assist in completing assessments. Problem-handling assessments will be drawn from typical industry applications and data sources.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit includes a summative assessment item conducted during the semester and a final examination. The summative assessment provides students with a hands-on opportunity to address practical issues encountered in machine learning. Through this task, students will learn to select appropriate methods for specific problems, supported by similar exercises in tutorials and practical sessions. Further details about the assessment task are provided below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (Applied)
Design and implement machine learning projects based on data analysis scenarios. This includes interpreting and communicating results, as well as critically reflecting on the methods applied.
This assignment is eligible for the 48-hour late submission period and approved assignment extensions.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Assessment: Invigilated Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester learning content.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
All learning materials required to study this unit are provided via the unit Canvas site.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO1, ULO2, ULO3, Project (Applied), Invigilated Exam - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO4, Project (Applied), Invigilated Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Project (Applied) - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Project (Applied) - Demonstrate developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: Project (Applied)
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO1, ULO2, ULO3, Project (Applied), Invigilated Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO4, Project (Applied), Invigilated Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO5, Project (Applied) - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Project (Applied)
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, ULO2, Project (Applied), Invigilated Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, ULO4, Project (Applied), Invigilated Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Project (Applied), Invigilated Exam - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: ULO5, Project (Applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: ULO6, Project (Applied)