Students

ACST8044 – Statistical Learning for Risk Modelling I

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Ken Siu
ken.siu@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
STAT8310
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit begins with coverage of the key concepts of statistical learning, the basics of data analysis and modelling. Applications will include linear models and generalised linear models. The concepts underlying time series models and actuarial applications of time series models are also studied. Students performing satisfactorily well in both ACST8044 and ACST8045 will meet the requirements to earn credit from Exam SRM of the Society of Actuaries.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Demonstrate an understanding of the key concepts of statistical learning
  • ULO2: Explain the various concepts related to linear models and generalized linear models and perform related calculations, analyses and interpretations.
  • ULO3: Compare and contrast between linear models and other popular methods such as k-nearest neighbour, lasso and ridge regression.
  • ULO4: Demonstrate an understanding on the various concepts and components of time series models and perform related calculations and interpretations.

General Assessment Information

Late Submission Penalties

If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0.

Example 1 (out of 100):

If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

Example 2 (out of 30):

If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI assisted?
Formal examination 60% No Exam Period Individual No Observed
Formal examination: Test 20% No 21/04/2026 Individual No Observed
Professional practice: Risk modelling 20% No 29/05/2026 Individual Yes Open AI

Formal examination

Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: Exam Period
Weighting: 60%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Observed

The purpose of this assessment is for you to formally demonstrate the expertise you have gained in this unit.

You will participate in a 3-hour exam held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips.

Deliverable(s): Formal exam.

Individual assessment
On successful completion you will be able to:
  • Demonstrate an understanding of the key concepts of statistical learning
  • Explain the various concepts related to linear models and generalized linear models and perform related calculations, analyses and interpretations.
  • Compare and contrast between linear models and other popular methods such as k-nearest neighbour, lasso and ridge regression.
  • Demonstrate an understanding on the various concepts and components of time series models and perform related calculations and interpretations.

Formal examination: Test

Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: 21/04/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?: Observed

The purpose of this assessment is for you to demonstrate your understanding of statistical learning concepts and the key ideas and techniques related to linear and generalised linear models, including calculations, analyses, and interpretations.

 

You will complete a 90-minute in-class test designed to assess your ability to apply these concepts.

 

Skills in focus:

  • Discipline knowledge
  • Critical thinking and problem solving

 

Deliverable(s): Test

 

Individual assessment
On successful completion you will be able to:
  • Demonstrate an understanding of the key concepts of statistical learning
  • Explain the various concepts related to linear models and generalized linear models and perform related calculations, analyses and interpretations.

Professional practice: Risk modelling

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 29/05/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: Yes
AI assisted?: Open AI

The purpose of this assessment is for you to demonstrate your problem-solving skills in R, focusing on linear and generalised linear models and comparing them to other methods such as k-nearest neighbour, lasso, and ridge regression.

 

You will complete a series of problem-solving questions that require detailed solutions supported by R code.

 

Skills in focus:

  • Discipline knowledge
  • Digital skills
  • Critical thinking and problem solving

 

Deliverable(s): Written solutions with R code.

 

Individual assessment
On successful completion you will be able to:
  • Explain the various concepts related to linear models and generalized linear models and perform related calculations, analyses and interpretations.
  • Compare and contrast between linear models and other popular methods such as k-nearest neighbour, lasso and ridge regression.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

The unit will be delivered by weekly seminars. The unit material will be available for download from iLearn. Students will be required to use iLearn, R, PDF, Excel, Word, a non-programmable calculator and other resources to be mentioned on the iLearn page. A recommended reading is:

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2021) An Introduction to Statistical Learning with Applications in R. Springer: New York.

Some other references or recommended reading materials will be introduced on the iLearn page whenever appropriate. 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2026.03 of the Handbook