CAB330 Machine Learning for Decision Making
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: | CAB330 |
|---|---|
| Prerequisite(s): | DSB100 or DSB102 or CAB201 or IFB220 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
| 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 2 2026, Gardens Point, Internal
| Unit code: | CAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | DSB100 or DSB102 or CAB201 or IFB220 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
| Coordinator: | Richi Nayak | r.nayak@qut.edu.au |
Overview
Machine Learning has become a popular technology for decision-making, transforming an organisation's large data collection into actionable insights about customers and business processes. It has direct applications in several fields, including social networks, business processes, search engines, e-commerce, digital libraries, bioinformatics and web information systems. This unit provides fundamental knowledge and skills in data analytics and machine learning to support data-driven decision-making in diverse and interdisciplinary applications. You will learn about machine learning and data mining techniques, including classification, clustering and association mining, and explore how these AI methods can be applied to text and web usage data. This is an introductory unit, and the knowledge and skills developed here are relevant to all IT professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Analyse and solve real-world data and web-analytics problems through the application of AI theoretical knowledge and technical skills.
- Plan and manage AI projects effectively from the start and avoid pitfalls in data preparation, modelling, and results interpretation.
- Identify suitable opportunities for data and web mining, and seamlessly integrate AI solutions into the business and technical ecosystems of organisations.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
- Communicate clearly in written, oral and visual formats to specialist and non-specialist audiences.
Content
The following topics will be covered.
- Introduction to Machine Learning and Data Mining
- The knowledge discovery process and methodology;
- Data Preparation for Machine Learning
- Supervised Machine Learning: Classification and prediction
- Unsupervised Machine Learning: Clustering
- Link Analysis
- Text Mining and Natural Language Processing
- Web Mining including Recommender systems
Learning Approaches
This unit will be delivered through the following means:
- Pre-recorded lectures which provide the theoretical basis of the subject;
- Practicals (2 hours) allow you to apply theory to practical (industry data-driven) problems using available software tools and implementation exercises.
- Interactive Q&A session (1 hour) weekly
Feedback on Learning and Assessment
You can obtain feedback on your progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions. You are encouraged to contact the lecturer personally for seeking feedback. The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
Assessment
Overview
The assessments in this unit are designed for you to demonstrate a critical understanding of the machine learning-based data and web analytics concepts acquired during the lectures, as well as the application of these concepts in diverse and inter-disciplinary real-world application settings acquired during practicals. The exam will allow you to demonstrate your understanding of these machine learning methods and challenges associated with data and web analytics. Assessment criteria will be made available to you at the introduction of each assessment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Case Study
Predictive Data Analytics
Case Study 1 includes mining meaningful information from the underlying data by applying predictive mining techniques. The assessment item consists of two components, one of which is a group report submitted via Canvas and eligible for a 48-hour grace period. The other component of the assessment item (an individual component) takes the form of an oral presentation that occurs live in the practical class. As the nature of this component of the assessment doesn't satisfy the definition of an assignment, it is therefore not eligible for the 48-hour grace period.
Assessment: Project (applied)
Descriptive Data Mining
Application of clustering and link analysis on enterprise, document and web data. The assessment item consists of two components, one of which is a group report submitted via Canvas and eligible for a 48-hour grace period. The other component of the assessment item (an individual component) takes the form of an oral presentation that occurs live in the practical class. As the nature of this component of the assessment doesn't satisfy the definition of an assignment, it is therefore not eligible for the 48-hour grace period.
Assessment: Invigilated Examination
This will assess what you have learned from the entire semester. This exam will consist of MCQ and short and long form questions.
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
In addition to the prescribed textbook below, lecture notes and various selected papers from the literature will be provided via the unit Canvas site. You are strongly encouraged to read recommended references and articles pertaining to this unit.
Resource Materials
Prescribed text(s)
Author: J. Han and M. Kamber, Title: Data Mining Concepts and Techniques, Morgan Kaufmann, 2022, Fourth Edition
This book is available as an e-book in the library. This book mainly contains the material covered in lectures from week 1 to week 8. Sufficient materials will be provided to you via handouts or online links for the lectures from week 9 to week 13.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
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: Case Study, Project (applied), Invigilated Examination - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Case Study, Project (applied), Invigilated Examination - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Case Study, Project (applied), Invigilated Examination - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Case Study, Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: ULO5, Case Study, 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, Case Study, Project (applied), Invigilated Examination - 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: ULO3, Case Study, Project (applied), Invigilated Examination - 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: ULO1, ULO2, Case Study, Project (applied) - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO3, Case Study, Project (applied), Invigilated Examination - 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: ULO2, ULO4, Case Study, 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: ULO5, Case Study, 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: ULO1, ULO3, Case Study, Project (applied), Invigilated Examination
Unit Outline: Semester 2 2026, Online
| Unit code: | CAB330 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | DSB100 or DSB102 or CAB201 or IFB220 |
| Assumed Knowledge: | Familiarity with the following IT concepts at the introductory level: Elementary Statistics; Basic Database Concepts; Finding library resources; and Issues involved in aligning business technology and information systems are assumed knowledge. |
Overview
Machine Learning has become a popular technology for decision-making, transforming an organisation's large data collection into actionable insights about customers and business processes. It has direct applications in several fields, including social networks, business processes, search engines, e-commerce, digital libraries, bioinformatics and web information systems. This unit provides fundamental knowledge and skills in data analytics and machine learning to support data-driven decision-making in diverse and interdisciplinary applications. You will learn about machine learning and data mining techniques, including classification, clustering and association mining, and explore how these AI methods can be applied to text and web usage data. This is an introductory unit, and the knowledge and skills developed here are relevant to all IT professionals.
Learning Outcomes
On successful completion of this unit you will be able to:
- Analyse and solve real-world data and web-analytics problems through the application of AI theoretical knowledge and technical skills.
- Plan and manage AI projects effectively from the start and avoid pitfalls in data preparation, modelling, and results interpretation.
- Identify suitable opportunities for data and web mining, and seamlessly integrate AI solutions into the business and technical ecosystems of organisations.
- Collaborate effectively in small teams to optimise managerial decision-making in AI projects.
- Communicate clearly in written, oral and visual formats to specialist and non-specialist audiences.
Content
The following topics will be covered.
- Introduction to Machine Learning and Data Mining
- The knowledge discovery process and methodology;
- Data Preparation for Machine Learning
- Supervised Machine Learning: Classification and prediction
- Unsupervised Machine Learning: Clustering
- Link Analysis
- Text Mining and Natural Language Processing
- Web Mining including Recommender systems
Learning Approaches
This unit will be delivered through the following means:
- Pre-recorded lectures which provide the theoretical basis of the subject;
- Practicals (2 hours) allow you to apply theory to practical (industry data-driven) problems using available software tools and implementation exercises.
- Interactive Q&A session (1 hour) weekly
Feedback on Learning and Assessment
You can obtain feedback on your progress throughout the unit through asking the teaching staff for advice and assistance during lectures and practical sessions. You are encouraged to contact the lecturer personally for seeking feedback. The assessments will be marked according to a criteria sheet and returned to you within two weeks of submission.
Assessment
Overview
The assessments in this unit are designed for you to demonstrate a critical understanding of the machine learning-based data and web analytics concepts acquired during the lectures, as well as the application of these concepts in diverse and inter-disciplinary real-world application settings acquired during practicals. The exam will allow you to demonstrate your understanding of these machine learning methods and challenges associated with data and web analytics. Assessment criteria will be made available to you at the introduction of each assessment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Case Study
Predictive Data Analytics
Case Study 1 includes mining meaningful information from the underlying data by applying predictive mining techniques. The assessment item consists of two components, one of which is a group report submitted via Canvas and eligible for a 48-hour grace period. The other component of the assessment item (an individual component) takes the form of an oral presentation that occurs live in the practical class. As the nature of this component of the assessment doesn't satisfy the definition of an assignment, it is therefore not eligible for the 48-hour grace period.
Assessment: Project (applied)
Descriptive Data Mining
Application of clustering and link analysis on enterprise, document and web data. The assessment item consists of two components, one of which is a group report submitted via Canvas and eligible for a 48-hour grace period. The other component of the assessment item (an individual component) takes the form of an oral presentation that occurs live in the practical class. As the nature of this component of the assessment doesn't satisfy the definition of an assignment, it is therefore not eligible for the 48-hour grace period.
Assessment: Invigilated Examination
This will assess what you have learned from the entire semester. This exam will consist of MCQ and short and long form questions.
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
In addition to the prescribed textbook below, lecture notes and various selected papers from the literature will be provided via the unit Canvas site. You are strongly encouraged to read recommended references and articles pertaining to this unit.
Resource Materials
Prescribed text(s)
Author: J. Han and M. Kamber, Title: Data Mining Concepts and Techniques, Morgan Kaufmann, 2022, Fourth Edition
This book is available as an e-book in the library. This book mainly contains the material covered in lectures from week 1 to week 8. Sufficient materials will be provided to you via handouts or online links for the lectures from week 9 to week 13.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. It is your responsibility to familiarise yourself with the Health and Safety policies and procedures applicable within campus areas and laboratories.
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: Case Study, Project (applied), Invigilated Examination - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Case Study, Project (applied), Invigilated Examination - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Case Study, Project (applied), Invigilated Examination - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Case Study, Project (applied) - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: ULO5, Case Study, 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, Case Study, Project (applied), Invigilated Examination - 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: ULO3, Case Study, Project (applied), Invigilated Examination - 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: ULO1, ULO2, Case Study, Project (applied) - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO3, Case Study, Project (applied), Invigilated Examination - 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: ULO2, ULO4, Case Study, 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: ULO5, Case Study, 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: ULO1, ULO3, Case Study, Project (applied), Invigilated Examination