MXN441 Advanced Statistical 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: | MXN441 |
|---|---|
| Prerequisite(s): | ((MXN601 or admission into MS10). MXN601 can be studied in the same teaching period as MXN441) OR (admission into IF80). |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,900 |
| International unit fee | $5,220 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | MXN441 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | ((MXN601 or admission into MS10). MXN601 can be studied in the same teaching period as MXN441) OR (admission into IF80). |
| Coordinator: | Mahdi Abolghasemi | mahdi.abolghasemi@qut.edu.au |
Overview
This unit introduces you to foundational and advanced concepts in statistical machine learning, equipping students with essential and advanced 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 machines, and neural networks. Additionally, the unit explores key clustering techniques and practical applications relevant to data-driven decision-making. Through a combination of lectures, tutorials, and both individual and group assignments in the form of research, you will engage deeply with statistical machine learning problems in theory and practice.
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 or in collaboration with others to apply problem-solving skills and develop practical solutions.
- Summarise and explain in written and/or oral form, the motivation, details and results of a statistical analysis of a data set.
Content
You will explore both supervised and unsupervised learning techniques, inlcuding linear regression and advancing to methods like decision trees, support vector machines, neural networks, logistic regression and several clustering methods.
Learning Approaches
The teaching and learning approaches will foster both acquisition of new knowledge at an advanced level and development of your skills. The approaches aim to facilitate your individual understanding of the key concepts and issues that are common to statistical machine learning in extended and complex problems and applications. Strategies also aim to assist you to develop your professional and life-long learning skills within selected realistic contexts that provide demonstration of key issues and methodology. Teaching and learning activities include problems in context and case studies to facilitate your discussion and to provide a learning environment in which you can develop your problem-solving skills.
You are expected to work not only in any lecture/workshop session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented by working through a wide variety of exercises, problems and online learning activities in your own time.
Feedback on Learning and Assessment
Feedback will be provided by academic staff through summative assessment tasks and through formative feedback during timetabled classes. You will also have opportunities for peer-to-peer learning and self-reflection to support your learning and skills development.
Assessment
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will submit solutions to several problem solving tasks involving stastical analysis, modelling and computation.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
You will be required to solve complex problems motivated by real-world datasets and scientific articles. You will be tasked with applying statistical and machine learning approaches to solve the problem; developing computer code that is appropriately formatted and commented to allow for re-use; and delivering a report and an oral presentation (20% of total mark) that describes your methodology and summarises key results and findings.
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.
The written report 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
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.MS10 Bachelor of Mathematics (Honours)
- Demonstrate and apply advanced knowledge and skills in mathematical sciences to critically analyse and solve complex problems within the discipline or in cross-disciplinary fields where mathematics underpins innovation
Relates to: Problem Solving Task, Project (applied) - Communicate complex concepts, methods and findings in the mathematical sciences clearly and effectively to a range of audiences including mathematicians, industry professionals and the general public, using a range of academic, professional, and technical formats
Relates to: Problem Solving Task, Project (applied) - Demonstrate autonomy, accountability, ethical scholarship, and effective collaboration for research and continuous learning, consistent with professional practice in the mathematical sciences
Relates to: Project (applied)