DSB102 Introduction to 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: | DSB102 |
|---|---|
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | DSB102 |
|---|---|
| Credit points: | 12 |
| Coordinator: | Mahdi Abolghasemi | mahdi.abolghasemi@qut.edu.au |
Overview
This unit introduces you to foundational concepts in statistical machine learning, equipping them with essential skills to handle and analyse complex data. You will explore both supervised and unsupervised learning techniques, starting with linear regression and advancing to methods like decision trees, support vector regression, and introductory neural networks. Additionally, the unit covers essential clustering techniques and simple yet practical machine learning applications suitable for first-year data science students. Through a combination of lectures, tutorials, and both individual and group assignments, you will engage deeply with real-world problems, and have the opportunity to benefit from diverse perspectives and career supports to develop their employability. You will be prepared to apply these methods and use industry-relevant digital practices to a range of real-world data problems and lay the groundwork for advanced studies in data science.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain key concepts in statistical machine learning including supervised and unsupervised learning, classification, and regression
- Formulate and implement various statistical machine learning algorithms in Python or R programming language and apply them to solve data science problems.
- Work both independently and in collaboration with others to apply problem-solving skills and develop practical solutions.
- Communicate statistical machine learning solutions effectively through written reports and presentations.
- Identify and explain ethical implications of data usage, modeling, and responsible deployment of machine learning systems.
- Utilise an online portfolio for evidencing skill development to enhance future career opportunities.
Content
You will explore both supervised and unsupervised learning techniques, starting with linear regression and advancing to methods like decision trees, support vector machines, and introductory neural networks. The course will cover logistic regression and unsupervised techniques including k-mean clustering and hierarchical clustering.
Learning Approaches
The teaching and learning approaches in this unit are designed to support your acquisition of
new knowledge and the development of practical skills in statistical machine learning. Through
a combination of lectures, tutorials, and both individual and group assignments, you will
engage deeply with key concepts and techniques. These methods aim to enhance your
individual understanding of fundamental issues and methods in statistical learning, from basic
to more advanced applications.
The teaching strategies will also focus on developing your professional and lifelong learning skills. By working with realistic problems and case studies, you will gain experience in applying statistical methods to practical data challenges in business, sustainability, and societal issues, fostering your problem-solving abilities in a supportive learning environment.
You are expected to engage actively during all allocated lecture and tutorial sessions and to
extend your learning through independent study. This includes consolidating the material
covered in class by completing a variety of exercises, problems, and activities outside of
scheduled teaching times.
Feedback on Learning and Assessment
You will gain feedback in this unit by participating in weekly online discussion forums and
fortnightly intensive workshops with community partners, academics and peers. You will also
receive written feedback for Assignment 1 and 3, and oral feedback for Assignment 2 which
will directly relate to and inform your final assessment.
Assessment
Overview
This unit includes three assessments that develop and evaluate a range of skills in machine learning. Students will engage in an individual Lab Demonstration task (40%) to apply core techniques, a group Presentation (10%) to communicate their approach and findings, and a team-based Project Report (50%) that involves end-to-end project work, including analysis, modelling, and reflection. Assessments support both independent and collaborative learning while targeting key unit learning outcomes.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Lab Demonstration
You will submit a combination of both long and short answer workbook problems focussing on programming and applications of techniques in lab.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is not eligible for the 48-hour late submission period and assignment extensions.
Assessment: Presentation
Students will work on an industry project in a group and prepare presentation slides to present their findings and solutions for the given problem to a panel of industry partners and teaching staff.
The late submission period does not apply and no assignment extensions are available.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Assessment: Project Report
Students will submit their written reports for the group project (details will be provided). Also, each student will submit their individual reflection on the unit and group project, and what that means for their career. The students will work on their project activities including use of GenAI.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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
Lecture notes and tutorial materials, or directions to references will be provided in the Canvas site.
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
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: Lab Demonstration, Project Report - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Lab Demonstration, Presentation, Project Report - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Presentation - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Presentation, Project Report - 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: Presentation - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Presentation
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, Lab Demonstration, Project Report - 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, Lab Demonstration, Presentation, Project Report - 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: ULO2 - 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, Lab Demonstration, Presentation, Project Report - 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: ULO4, Presentation, Project Report - 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, ULO2, ULO3, ULO5, Project Report - 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: ULO5, Project Report
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Lab Demonstration, Project Report - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Lab Demonstration, Project Report - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Lab Demonstration, Presentation - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Presentation, Project Report - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Presentation, Project Report - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Presentation, Project Report
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Lab Demonstration, Project Report - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Lab Demonstration, Project Report - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Lab Demonstration, Presentation - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Presentation, Project Report - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Presentation - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Presentation
Unit Outline: Semester 2 2026, Online
| Unit code: | DSB102 |
|---|---|
| Credit points: | 12 |
Overview
This unit introduces you to foundational concepts in statistical machine learning, equipping them with essential skills to handle and analyse complex data. You will explore both supervised and unsupervised learning techniques, starting with linear regression and advancing to methods like decision trees, support vector regression, and introductory neural networks. Additionally, the unit covers essential clustering techniques and simple yet practical machine learning applications suitable for first-year data science students. Through a combination of lectures, tutorials, and both individual and group assignments, you will engage deeply with real-world problems, and have the opportunity to benefit from diverse perspectives and career supports to develop their employability. You will be prepared to apply these methods and use industry-relevant digital practices to a range of real-world data problems and lay the groundwork for advanced studies in data science.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain key concepts in statistical machine learning including supervised and unsupervised learning, classification, and regression
- Formulate and implement various statistical machine learning algorithms in Python or R programming language and apply them to solve data science problems.
- Work both independently and in collaboration with others to apply problem-solving skills and develop practical solutions.
- Communicate statistical machine learning solutions effectively through written reports and presentations.
- Identify and explain ethical implications of data usage, modeling, and responsible deployment of machine learning systems.
- Utilise an online portfolio for evidencing skill development to enhance future career opportunities.
Content
You will explore both supervised and unsupervised learning techniques, starting with linear regression and advancing to methods like decision trees, support vector machines, and introductory neural networks. The course will cover logistic regression and unsupervised techniques including k-mean clustering and hierarchical clustering.
Learning Approaches
The teaching and learning approaches in this unit are designed to support your acquisition of
new knowledge and the development of practical skills in statistical machine learning. Through
a combination of lectures, tutorials, and both individual and group assignments, you will
engage deeply with key concepts and techniques. These methods aim to enhance your
individual understanding of fundamental issues and methods in statistical learning, from basic
to more advanced applications.
The teaching strategies will also focus on developing your professional and lifelong learning skills. By working with realistic problems and case studies, you will gain experience in applying statistical methods to practical data challenges in business, sustainability, and societal issues, fostering your problem-solving abilities in a supportive learning environment.
You are expected to engage actively during all allocated lecture and tutorial sessions and to
extend your learning through independent study. This includes consolidating the material
covered in class by completing a variety of exercises, problems, and activities outside of
scheduled teaching times.
Feedback on Learning and Assessment
You will gain feedback in this unit by participating in weekly online discussion forums and
fortnightly intensive workshops with community partners, academics and peers. You will also
receive written feedback for Assignment 1 and 3, and oral feedback for Assignment 2 which
will directly relate to and inform your final assessment.
Assessment
Overview
This unit includes three assessments that develop and evaluate a range of skills in machine learning. Students will engage in an individual Lab Demonstration task (40%) to apply core techniques, a group Presentation (10%) to communicate their approach and findings, and a team-based Project Report (50%) that involves end-to-end project work, including analysis, modelling, and reflection. Assessments support both independent and collaborative learning while targeting key unit learning outcomes.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Lab Demonstration
You will submit a combination of both long and short answer workbook problems focussing on programming and applications of techniques in lab.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is not eligible for the 48-hour late submission period and assignment extensions.
Assessment: Presentation
Students will work on an industry project in a group and prepare presentation slides to present their findings and solutions for the given problem to a panel of industry partners and teaching staff.
The late submission period does not apply and no assignment extensions are available.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Assessment: Project Report
Students will submit their written reports for the group project (details will be provided). Also, each student will submit their individual reflection on the unit and group project, and what that means for their career. The students will work on their project activities including use of GenAI.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
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
Lecture notes and tutorial materials, or directions to references will be provided in the Canvas site.
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
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: Lab Demonstration, Project Report - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Lab Demonstration, Presentation, Project Report - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Presentation - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Presentation, Project Report - 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: Presentation - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Presentation
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, Lab Demonstration, Project Report - 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, Lab Demonstration, Presentation, Project Report - 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: ULO2 - 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, Lab Demonstration, Presentation, Project Report - 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: ULO4, Presentation, Project Report - 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, ULO2, ULO3, ULO5, Project Report - 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: ULO5, Project Report
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Lab Demonstration, Project Report - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Lab Demonstration, Project Report - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Lab Demonstration, Presentation - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Presentation, Project Report - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Presentation, Project Report - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Presentation, Project Report
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Lab Demonstration, Project Report - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Lab Demonstration, Project Report - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Lab Demonstration, Presentation - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Lab Demonstration, Presentation - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Presentation, Project Report - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Presentation - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Presentation