Statistical Learning (STAT3006)

Course overview

Statistics is a key building block for numerous modern machine learning algorithms that have found success in many important applications. This course introduces the core ideas behind these algorithms from a statistical and mathematical perspective, providing the depth of understanding needed for students to apply the methods with awareness of their strengths and limitations. The material covered includes probabilistic and analytic foundations, multivariate statistical analysis, and a broad range of machine learning techniques, with particular emphasis on clustering, classification, model selection and high‑dimensional analysis. Students will develop both theoretical insight and practical skills by working with real and simulated datasets, applying the methods in practice and examining their behaviour. Practical components will involve programming in Python.

This course provides a rigorous introduction to machine learning, covering both the mathematical foundations and practical methodologies used in modern data analysis. It assumes some prior knowledge of linear algebra, calculus, statistics and probability theory, and programming in Python, with students responsible for addressing any gaps in their background. The course explores a wide range of supervised learning techniques, from fundamental methods such as k‑nearest neighbours, decision trees, and linear and logistic regression to advanced approaches including deep neural networks, support vector machines, Gaussian processes, random forests and boosting. It also covers key unsupervised learning methods, including generative modelling, k‑means clustering, principal component analysis (PCA), autoencoders and generative adversarial networks (GANs). Students will develop both theoretical understanding and practical skills through problem‑solving with real and simulated datasets, gaining insight into the strengths, limitations and appropriate use of different models. Assessment tasks involve mathematical derivations, analytical exercises, application and extension of machine‑learning methods, report writing and programming in Python.

In the School of Mathematics and Physics we are committed to creating an inclusive and empowering learning environment for all students. We value and respect the diverse range of experiences our students bring to their education, and we believe that this diversity is crucial for fostering a rich culture of knowledge sharing and meaningful exploration. We hold both students and staff accountable for actively contributing to the establishment of a respectful and supportive learning environment.

Bullying, harassment, and discrimination in any form are strictly against our principles and against UQ Policy, and will not be tolerated. We have developed a suite of resources to assist you in recognising, reporting, and addressing such behaviour. If you have any concerns about your experience in this course, we encourage you to tell a member of the course teaching team, or alternatively contact an SMP Classroom Inclusivity Champion (see Blackboard for contact details). Our Inclusivity Champions are here to listen, to understand your concerns, and to explore potential actions that can be taken to resolve them. Your well-being and a positive learning atmosphere are of utmost importance to us.

Course requirements

Assumed background

Students are expected to possess a substantial background in mathematics and statistics, including linear algebra (e.g., vectors, matrices, eigenvalues), mathematical analysis (e.g. multivariate derivatives and integration), probability (e.g. joint and conditional distributions), and statistical methods (e.g., confidence intervals, multiple linear regression).

In addition, students need to have programming experience suitable for implementing and modifying statistical and machine learning algorithms in Python. They must be able to write, use and submit readable, functional code as part of course assignments. Students will require access to a computer or laptop on which they can install and use Python and associated packages.

Students should also be able to make reasonable methodological decisions and justify these in writing, interpret and discuss theoretical and empirical results insightfully, and communicate their conclusions clearly. They need to have, or be prepared to develop, the ability to locate, read and learn from relevant published work.

It is recommended that students take this course in their third or later years, after they have built sufficient maturity in mathematics, statistics and programming, preferably with Python. Prior or concurrent courses covering machine learning are not required but may offer useful familiarity. Of the introductory statistics courses leading into this course, STAT1301 and STAT2203 are the most suitable options due to their more mathematical orientation.

Further background in mathematics, probability and statistics is recommended through the suggested pre‑ and co‑requisites.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(STAT1201 or STAT1301 or STAT2203) + (MATH1051 or MATH1071) + (MATH1052 or MATH1072) + (MATH2504 or CSSE1001 or ENGG1001)

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

(MATH2001 or MATH2901), STAT2003, (STAT2004 or STAT2904)

Recommended companion or co-requisite courses

We recommend completing the following courses at the same time:

MATH3204, STAT3001, COMP4702

Incompatible

You can't enrol in this course if you've already completed the following:

STAT7305 (co-taught)

Course contact

Course staff

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

All classes will be conducted on campus. Consult your personal timetable for times and locations. Students are expected to attend these sessions inﾠperson unless they have a valid reason for being unable to attend (such as illness).ﾠ

Applied classes start in week 2. If an applied classes falls on a public holiday, there will be no make-up session. Instead, students are welcome to attend any other session that fits their schedule.

Aims and outcomes

On completing this course, students will have obtained knowledge on advanced topics in multivariate statistical analysis and machine learning. They will have demonstrated their ability to find appropriate techniques in the literature and apply these to a given problem, critically evaluate the results and the methods and communicate the results.

Learning outcomes

After successfully completing this course you should be able to:

Assessment

Assessment summary

Assessment details

Assignments

Mode

Written

Category

Tutorial/ Problem Set

Weight

45% , 15% each

Due date

Assignment 01 20/03/2026 5:00 pm

Assignment 02 1/05/2026 5:00 pm

Assignment 03 29/05/2026 5:00 pm

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Each assignment is to be submitted via Blackboard as a single pdf document.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Solutions for assessment item/s will be released 7 calendar days after the assessment is due and as such, an extension after 7 calendar days will not be possible.

See ADDITIONAL ASSESSMENT INFORMATION for extension and deferred examination information relating to this assessment item.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Solutions for assignments will be released 7 days after the assessment is due and as such, late submission after 7 days from the original time submission is due will not be possible, regardless of approved extensions.

End of Semester Exam

• Identity Verified
• In-person

Mode

Written

Category

Examination

Weight

55%

Due date

End of Semester Exam Period

6/06/2026 - 20/06/2026

Other conditions

Secure.

See the conditions definitions

Learning outcomes

L01, L02, L03, L05

Task description

Exam details

Submission guidelines

Deferral or extension

You may be able to defer this exam.

See ADDITIONAL ASSESSMENT INFORMATION for extension/deferral information relating to this assessment item.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Supplementary assessment

Supplementary assessment is available for this course.

Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply. 

Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.  

If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met.  

The main supplementary assessment in this course will be a 2-hour examination similar in style to the end-of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment.

Additional assessment information

Artificial Intelligence

To pass this course, students will be required to demonstrate a detailed understanding of course material together with a range of associated skills independent of Artificial Intelligence (AI) and Machine Translation (MT) tools.

For assessment tasks that are completed in-person (including examinations) termed “secure assessment”, the use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted unless otherwise advised. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.

Other non-secure assessment tasks (such as assignments) are designed to help you develop your understanding and skills, and to prepare you for secure assessment. You are thus generally encouraged to complete such assessment without the use of AI/MT, unless explicitly advised to the contrary in the assessment item. In any event, if you choose to use such tools, then you must clearly reference any such use within your submitted work. A failure to reference AI or MT use may constitute student misconduct under the Student Code of Conduct.

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

• Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
• Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
• If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
• If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
• If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
• For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for details. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
• An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
• If you have been ill or unable to attend class for more than 14 days, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
• Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
• There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.

 Applications to defer an exam

In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.

Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.

Please note:

• Applications can be submitted no later than 5 calendar days after the date of the original exam.
• There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
• Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
• All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
• You’ll receive an email to your student email account when the status of your application is updated.
• If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Library resources are available on the UQ Library website.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

• Student Code of Conduct Policy
• Student Integrity and Misconduct Policy and Procedure
• Assessment Procedure
• Examinations Procedure
• Reasonable Adjustments for Students Policy and Procedure
• AI for Assessment Guide

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.