Statistical Learning (STAT7305)

Course overview

Statistics is a key building block for numerous modern machine learning algorithms that have found success in many important applications. The course will cover key ideas behind these algorithms from a statistical perspective, and provide an in-depth knowledge that will enable students to apply the methods with awareness of their strengths and limitations. The topics covered will include probabilistic and analytic foundations, multivariate statistical analysis and machine learning, with a particular focus on clustering, classification, model selection and high-dimensional statistical analysis. Students will gain a theoretical understanding of these techniques and develop practical skills by using the R statistical programming language to solve problems involving various real and simulated datasets.

This course covers topics in multivariate statistical analysis and machine learning, including mathematical foundations, hypothesis testing, clustering, density estimation, classification, regression, model selection and high-dimensional analysis. Students will gain a theoretical understanding of these techniques and develop practical skills by solving problems involving real and simulated datasets, with both contributing to understanding strengths and weaknesses. Assessment tasks will include mathematical derivations, analysis, application and extension of statistical and machine learning models, report writing and programming with R or Python.

Course requirements

Assumed background

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

Additionally, students need to have some programming skills, particularly for implementing and modifying statistics and machine learning algorithms with R or Python. Students will need to write, use and submit readable and functional code as part of course assignments. They will need to have access to a computer or laptop, and be able to install and use R or Python and associated packages.

Students also need to have the ability to make reasonable decisions and justify these in writing, to interpret and discuss results and theoretical aspects insightfully and communicate this in writing. They need to have or develop the ability to find, read and learn from relevant published work.

Prerequisites

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

STAT2004 or DATA7202 or ((COMP4702 or COMP7703) AND (MATH1052 or MATH7502))

Recommended prerequisites

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

STAT3001, DATA7202, COMP7703

Incompatible

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

STAT3006 (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 in person unless they have a valid reason for being unable to attend (such as illness). Important: if you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details.

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

Assignment 1

Mode

Written

Category

Computer Code, Project

Weight

20%

Due date

23/08/2024 5:00 pm

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Via Blackboard: .pdf for report; code and any supplementary information via .zip file.

Deferral or extension

You may be able to apply for an extension.

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.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Assignment 2 - Postgraduate

Mode

Written

Category

Computer Code, Project

Weight

30%

Due date

20/09/2024 5:00 pm

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Via Blackboard: .pdf for report, code and any supplementary information via .zip file.

Deferral or extension

You may be able to apply for an extension.

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.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Assignment 3 - Postgraduate

Mode

Written

Category

Computer Code, Project

Weight

30%

Due date

18/10/2024 5:00 pm

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Via Blackboard: .pdf for report, code and any supplementary information via .zip file.

Deferral or extension

You may be able to apply for an extension.

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.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

Assignment 4

Mode

Written

Category

Computer Code, Project

Weight

20%

Due date

11/11/2024 5:00 pm

Learning outcomes

L01, L02, L03, L04, L05

Task description

Submission guidelines

Via Blackboard: .pdf for report, code and any supplementary information via .zip file.

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.

Assignment 4 is due during the second week of examinations, due to marking constraints, it can be extended a maximum of 7 days. 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.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item then the listed penalty will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due.

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.  

Supplementary assessment in this course will be an additional assignment following the same format as the primary course assessment tasks but with varied questions. 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

The assessment tasks in this course evaluate students’ abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute 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.

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

Find the required and recommended resources for this course 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 - Students Policy and Procedure

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