IFN580 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: | IFN580 |
|---|---|
| Prerequisite(s): | (IFN581 or IFN509 or IFN556 or IFQ556 or IFN555 or IFQ555) OR (192cps in IV04 or IV05 or EV08 or EV07) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN17). |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $5,352 |
Unit Outline: Semester 1 2025, Gardens Point, Internal
| Unit code: | IFN580 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN581 or IFN509 or IFN556 or IFQ556 or IFN555 or IFQ555) OR (192cps in IV04 or IV05 or EV08 or EV07) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN17). |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
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 essential 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, encompassing various aspects of traditional supervised and deep learning techniques and unsupervised, semi-supervised, transfer learning and reinforcement learning concepts. They assist students in developing a comprehensive understanding of machine learning and its practical applications.
Learning Approaches
This subject will be delivered through the following means:
Lectures (2 hours), which provide the theoretical basis of the subject.
Tutorials/Practicals (2 hours) provide an opportunity to work in groups 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, laboratory 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
You can obtain feedback on your progress throughout the unit by asking the teaching staff for advice and assistance during lectures and practical sessions.
The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
You can request private consultation with the teaching staff.
Assessment
Overview
This unit includes two summative assessment items during the semester and a final examination. Summative assessments are designed to give students a hands-on approach to dealing with various issues that arise in machine learning. Students will learn how to choose a particular method appropriate to the problem at hand, and similar tasks provided in tutorials and practicals will help them complete the assessment items successfully. More details of each assessment task are given below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Develop solutions by applying supervised machine learning techniques to solve the given problems.
This is an assignment for the purposes of an extension.
Assessment: Project (Applied)
Design and implement a machine learning project based on analysis of a data analysis scenario, including interpretation and communication of results along with critical reflection regarding the applied methods.
This is an assignment for the purposes of an extension.
Assessment: On campus (in-person) Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester learning content.
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.
Requirements to Study
Costs
No extraordinary charges or costs are associated with the requirements for this unit.
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.IN17 Graduate Certificate in Communication for Information Technology
- Demonstrate an advanced knowledge of information technology disciplines.
Relates to: ULO1, ULO2, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO1, ULO2, ULO3, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO4, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Problem Solving Task, Project (Applied) - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (Applied)
IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, ULO2, Problem Solving Task, Project (Applied), On campus (in-person) 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, Problem Solving Task, Project (Applied), On campus (in-person) 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, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: ULO5, Problem Solving Task, 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, Problem Solving Task, Project (Applied)
IN28 Master of Artificial Intelligence
- Demonstrate advanced specialist IT knowledge in Artificial Intelligence discipline.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex Artificial Intelligence problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Integrate advanced, industry-best practice, Artificial Intelligence methods, tools and techniques to develop and implement complex Artificial Intelligence systems, processes and/or software.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively in Artificial Intelligence professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: Problem Solving Task, Project (Applied)
IN29 Master of Cyber Security
- Demonstrate advanced specialist IT knowledge in Cyber Security discipline.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex Cyber Security problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Integrate advanced, industry-best practice, Cyber Security methods, tools and techniques to develop and implement complex Cyber Security systems, processes and/or software.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively in Cyber Security professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: Problem Solving Task, Project (Applied)
IN30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (Applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (Applied)
IN31 Master of Data Science
- Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work effectively both independently and collaboratively in project teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively and succinctly in oral, written and visual formats for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (Applied)
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | IFN580 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN581 or IFN509 or IFN556 or IFQ556 or IFN555 or IFQ555) OR (192cps in IV04 or IV05 or EV08 or EV07) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN17). |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
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 essential 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, encompassing various aspects of traditional supervised and deep learning techniques and unsupervised, semi-supervised, transfer learning and reinforcement learning concepts. They assist students in developing a comprehensive understanding of machine learning and its practical applications.
Learning Approaches
This subject will be delivered through the following means:
Lectures (2 hours), which provide the theoretical basis of the subject.
Tutorials/Practicals (2 hours) provide an opportunity to work in groups 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, laboratory 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
You can obtain feedback on your progress throughout the unit by asking the teaching staff for advice and assistance during lectures and practical sessions.
The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
You can request private consultation with the teaching staff.
Assessment
Overview
This unit includes two summative assessment items during the semester and a final examination. Summative assessments are designed to give students a hands-on approach to dealing with various issues that arise in machine learning. Students will learn how to choose a particular method appropriate to the problem at hand, and similar tasks provided in tutorials and practicals will help them complete the assessment items successfully. More details of each assessment task are given below.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
Develop solutions by applying supervised machine learning techniques to solve the given problems.
This is an assignment for the purposes of an extension.
Assessment: Project (Applied)
Design and implement a machine learning project based on analysis of a data analysis scenario, including interpretation and communication of results along with critical reflection regarding the applied methods.
This is an assignment for the purposes of an extension.
Assessment: On campus (in-person) Exam
Quiz / short answer questions and more advanced problems covering the whole of the semester learning content.
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.
Requirements to Study
Costs
No extraordinary charges or costs are associated with the requirements for this unit.
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.IN17 Graduate Certificate in Communication for Information Technology
- Demonstrate an advanced knowledge of information technology disciplines.
Relates to: ULO1, ULO2, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate solutions.
Relates to: ULO1, ULO2, ULO3, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO4, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work effectively in both self-directed and collaborative contexts.
Relates to: ULO5, Problem Solving Task, Project (Applied) - Communicate effectively in IT professional contexts using written, visual and oral formats.
Relates to: ULO6, Problem Solving Task, Project (Applied)
IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, ULO2, Problem Solving Task, Project (Applied), On campus (in-person) 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, Problem Solving Task, Project (Applied), On campus (in-person) 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, Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: ULO5, Problem Solving Task, 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, Problem Solving Task, Project (Applied)
IN28 Master of Artificial Intelligence
- Demonstrate advanced specialist IT knowledge in Artificial Intelligence discipline.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex Artificial Intelligence problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Integrate advanced, industry-best practice, Artificial Intelligence methods, tools and techniques to develop and implement complex Artificial Intelligence systems, processes and/or software.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively in Artificial Intelligence professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: Problem Solving Task, Project (Applied)
IN29 Master of Cyber Security
- Demonstrate advanced specialist IT knowledge in Cyber Security discipline.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically analyse complex Cyber Security problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Integrate advanced, industry-best practice, Cyber Security methods, tools and techniques to develop and implement complex Cyber Security systems, processes and/or software.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively in Cyber Security professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: Problem Solving Task, Project (Applied)
IN30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (Applied) - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (Applied)
IN31 Master of Data Science
- Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
Relates to: Problem Solving Task, Project (Applied), On campus (in-person) Exam - Work effectively both independently and collaboratively in project teams to enable successful processes and outcomes.
Relates to: Problem Solving Task, Project (Applied) - Communicate effectively and succinctly in oral, written and visual formats for diverse purposes and audiences.
Relates to: Problem Solving Task, Project (Applied)