CAB420 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: | CAB420 |
|---|---|
| Prerequisite(s): | (CAB201 or EGB202 or CAB202 or ITD121 or IFN501 or IFN556 or Admission to (EN50 or EN55 or EN52 or EN56 or EN57 or EN62 or EN72)) or (192cps in SV03 or IV04 or MV05 or EV08) or (144cps in EV10) or (enrolment in IV53 or IV54 or IV55 or IV56 or IV58). |
| Antirequisite(s): | IFN580 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,704 |
| International unit fee | $5,640 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | CAB420 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB201 or EGB202 or CAB202 or ITD121 or IFN501 or IFN556 or Admission to (EN50 or EN55 or EN52 or EN56 or EN57 or EN62 or EN72)) or (192cps in SV03 or IV04 or MV05 or EV08) or (144cps in EV10) or (enrolment in IV53 or IV54 or IV55 or IV56 or IV58). |
| Anti-requisite: | IFN580 |
| Coordinator: | Simon Denman | s.denman@qut.edu.au |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This unit provides you with a broad introduction to machine learning and its statistical foundations. Topics include: definition of machine learning tasks; classification principles and methods; dimensionality reduction/subspace methods; and deep learning methods such as convolutional neural networks and transformers. The unit makes use of python, jupyterlab, git and state of the art machine learning libraries. In addition to addressing specific machine learning methods, we will consider the ethical implications of machine learning in applications where individuals or groups could be marginalised, and the computational cost of machine learning methods and ways to reduce the compute burden. Application examples are taken from areas such as computer vision, finance, market prediction and information retrieval.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the principles and concepts of machine learning science using a range of tools and techniques.
- Critically evaluate different machine learning algorithms in a range of complex business, science, engineering, and health contexts
- Reflect on the ethical considerations that arise in applying machine learning in real-world settings
- Research cutting edge developments in machine learning and communicate findings to a specialised audience
- Critically analyse how artificial neural networks relate to the human brain and parallel hardware.
Content
The following topics will be covered:
- Introduction to Machine Learning
- Regression Techniques
- Classification Methods
- Dimensionality Reduction Methods
- Optimization in Machine Learning
- Deep Learning
- Machine Learning Applications including comptuer vision, audio processing, and forecasting
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops (2 hours per week, in person or online) and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. Relevant maths content will be embedded within the lecture sessions. Practical sessions will provide opportunities to explore the underlying theory, practice in the application of theory and algorithms, and allow exploration of concepts with tutors and other students. The assignments require an integrated understanding of the subject matter, and promote required knowledge and skills. You can expect to spend between 10 - 15 hours per week on average involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through criteria sheet grading.
- In addition to CRA (criteria sheet), comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Assessment
Overview
The assessment for this unit is comprised of three programming assignments, a final project, and a final exam (formal written examination).
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
This will consist of 2 small problem-solving tasks that explore the application of machine learning methods. Detailed descriptions will be released on the Canvas website under the 'assessment' section.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
An optional project proposal (2 page maximum): The project proposal should include the following information: project title, project idea, brief background, datasets, timeline and the team members. This will provide a chance for early feedback on your project.
Final Report: You must submit a report having the following sections: Introduction; Background; Proposed method; Analysis behind your approach; Details of the experiments; and Conclusions.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (written)
A set of questions on major concepts and problem solving from all the unit material.
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
Learning materials associated with this unit are available in the unit Canvas site.
There is no required text book. Contents from latest publications in top-tier machine learning journals will be used and referenced during the lectures.
Risk Assessment Statement
No particular risk is associated to this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Australian Computer Society Core Body of Knowledge
1: ICT Professional Knowledge
3: Technology Resources
- Hardware and software fundamentals
Relates to: ULO5
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Project (applied), Examination (written)
Relates to: Examination (written)
2: Engineering Application Ability
Relates to: Problem Solving Task, Examination (written)
Relates to: Problem Solving Task
Relates to: Problem Solving Task
3: Professional and Personal Attributes
Relates to: Problem Solving Task
Relates to: Project (applied)
Relates to: Project (applied)
Relates to: Project (applied)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Project (applied) - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Problem Solving Task, Examination (written)
EN01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: ULO3, Problem Solving Task - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO1, Problem Solving Task, Project (applied) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO2, ULO5, Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: ULO4, Project (applied)
EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN55 Master of Professional Engineering
- Apply advanced and specialist knowledge, concepts and practices in engineering design, analysis management and sustainability.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Critically analyse and evaluate complex engineering problems to achieve research informed solutions.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Apply systematic approaches to plan, design, execute and manage an engineering project.
Relates to: Problem Solving Task - Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non specialist audiences.
Relates to: Problem Solving Task, Project (applied)
EN62 Graduate Certificate in Robotics
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary practice in Robotics
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Analyse and evaluate Robotics problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Effectively communicate Robotics problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Project (applied)
EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Problem Solving Task, Project (applied), Examination (written) - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Project (applied)
EV01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: Project (applied) - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Project (applied) - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Project (applied) - Deploy appropriate approaches to engineering design and quality.
Relates to: Examination (written) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: Project (applied)
IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: ULO1, Problem Solving Task, Project (applied), Examination (written) - Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: ULO2, Problem Solving Task, Project (applied), Examination (written) - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: Project (applied) - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO4 - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: ULO3, ULO5, Project (applied) - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: Project (applied) - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: Problem Solving Task, Examination (written) - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: Problem Solving Task
Unit Outline: Semester 1 2026, Online
| Unit code: | CAB420 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB201 or EGB202 or CAB202 or ITD121 or IFN501 or IFN556 or Admission to (EN50 or EN55 or EN52 or EN56 or EN57 or EN62 or EN72)) or (192cps in SV03 or IV04 or MV05 or EV08) or (144cps in EV10) or (enrolment in IV53 or IV54 or IV55 or IV56 or IV58). |
| Anti-requisite: | IFN580 |
Overview
Machine learning is the science of getting computers to act without being explicitly programmed. This unit provides you with a broad introduction to machine learning and its statistical foundations. Topics include: definition of machine learning tasks; classification principles and methods; dimensionality reduction/subspace methods; and deep learning methods such as convolutional neural networks and transformers. The unit makes use of python, jupyterlab, git and state of the art machine learning libraries. In addition to addressing specific machine learning methods, we will consider the ethical implications of machine learning in applications where individuals or groups could be marginalised, and the computational cost of machine learning methods and ways to reduce the compute burden. Application examples are taken from areas such as computer vision, finance, market prediction and information retrieval.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the principles and concepts of machine learning science using a range of tools and techniques.
- Critically evaluate different machine learning algorithms in a range of complex business, science, engineering, and health contexts
- Reflect on the ethical considerations that arise in applying machine learning in real-world settings
- Research cutting edge developments in machine learning and communicate findings to a specialised audience
- Critically analyse how artificial neural networks relate to the human brain and parallel hardware.
Content
The following topics will be covered:
- Introduction to Machine Learning
- Regression Techniques
- Classification Methods
- Dimensionality Reduction Methods
- Optimization in Machine Learning
- Deep Learning
- Machine Learning Applications including comptuer vision, audio processing, and forecasting
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops (2 hours per week, in person or online) and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. Relevant maths content will be embedded within the lecture sessions. Practical sessions will provide opportunities to explore the underlying theory, practice in the application of theory and algorithms, and allow exploration of concepts with tutors and other students. The assignments require an integrated understanding of the subject matter, and promote required knowledge and skills. You can expect to spend between 10 - 15 hours per week on average involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through criteria sheet grading.
- In addition to CRA (criteria sheet), comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Assessment
Overview
The assessment for this unit is comprised of three programming assignments, a final project, and a final exam (formal written examination).
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
This will consist of 2 small problem-solving tasks that explore the application of machine learning methods. Detailed descriptions will be released on the Canvas website under the 'assessment' section.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
An optional project proposal (2 page maximum): The project proposal should include the following information: project title, project idea, brief background, datasets, timeline and the team members. This will provide a chance for early feedback on your project.
Final Report: You must submit a report having the following sections: Introduction; Background; Proposed method; Analysis behind your approach; Details of the experiments; and Conclusions.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (written)
A set of questions on major concepts and problem solving from all the unit material.
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
Learning materials associated with this unit are available in the unit Canvas site.
There is no required text book. Contents from latest publications in top-tier machine learning journals will be used and referenced during the lectures.
Risk Assessment Statement
No particular risk is associated to this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Australian Computer Society Core Body of Knowledge
1: ICT Professional Knowledge
3: Technology Resources
- Hardware and software fundamentals
Relates to: ULO5
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task, Project (applied), Examination (written)
Relates to: Examination (written)
2: Engineering Application Ability
Relates to: Problem Solving Task, Examination (written)
Relates to: Problem Solving Task
Relates to: Problem Solving Task
3: Professional and Personal Attributes
Relates to: Problem Solving Task
Relates to: Project (applied)
Relates to: Project (applied)
Relates to: Project (applied)
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Project (applied) - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Problem Solving Task, Examination (written)
EN01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: ULO3, Problem Solving Task - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: ULO1, Problem Solving Task, Project (applied) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: ULO2, ULO5, Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: ULO4, Project (applied)
EN52 Master of Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Robotics and AI
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to Robotics and AI problems to achieve research-informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN55 Master of Professional Engineering
- Apply advanced and specialist knowledge, concepts and practices in engineering design, analysis management and sustainability.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Critically analyse and evaluate complex engineering problems to achieve research informed solutions.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Apply systematic approaches to plan, design, execute and manage an engineering project.
Relates to: Problem Solving Task - Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non specialist audiences.
Relates to: Problem Solving Task, Project (applied)
EN62 Graduate Certificate in Robotics
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary practice in Robotics
Relates to: ULO4, Problem Solving Task, Project (applied), Examination (written) - Analyse and evaluate Robotics problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Effectively communicate Robotics problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: ULO4, Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Project (applied)
EN72 Master of Advanced Robotics and Artificial Intelligence
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, Project (applied), Examination (written) - Critically analyse, evaluate and apply appropriate methods to problems to achieve research-informed solutions in Advanced Robotics and AI and Data Analytics domains
Relates to: Problem Solving Task, Project (applied), Examination (written) - Communicate complex information effectively and succinctly in oral and written form for diverse purposes and audiences
Relates to: Project (applied) - Work independently and collaboratively demonstrating ethical and socially responsible practice
Relates to: Project (applied)
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Problem Solving Task, Project (applied), Examination (written) - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem Solving Task, Project (applied), Examination (written) - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Project (applied) - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Project (applied)
EV01 Bachelor of Engineering (Honours)
- Make decisions ethically within the social, cultural, and organisational contexts of professional engineering practice.
Relates to: Project (applied) - Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
Relates to: Project (applied) - Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
Relates to: Problem Solving Task, Project (applied) - Deploy appropriate approaches to engineering design and quality.
Relates to: Examination (written) - Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
Relates to: Problem Solving Task, Project (applied), Examination (written) - Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
Relates to: Project (applied)
IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: ULO1, Problem Solving Task, Project (applied), Examination (written) - Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: ULO2, Problem Solving Task, Project (applied), Examination (written) - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: Project (applied) - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO4 - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: ULO3, ULO5, Project (applied) - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: Project (applied) - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: Problem Solving Task, Examination (written) - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: Problem Solving Task