Students

ACST8045 – Statistical Learning for Risk Modelling II

2025 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Yanlin Shi
yanlin.shi@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
ACST8044 and STAT8310
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit begins with common methods of assessing model accuracy in statistical learning. This unit examines: principal components analysis, resampling methods, tree-based methods and clustering methods. 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 various concepts related to the assessment of model accuracy in statistical learning.
  • ULO2: Explain the basics and key concepts associated with decision tree models and its ensemble methods and perform related interpretations.
  • ULO3: Explain the uses and key concepts of principal component analysis and perform related interpretations.
  • ULO4: Demonstrate an understanding of the various concepts related to clustering methods.

General Assessment Information

Late Assessment Submission Penalty (written assessments)   

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern.    

For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.

Assessment Tasks

Name Weighting Hurdle Due
Formal and Observed Learning: Test 20% No 15/09/2025
Professional Practice: Risk Modelling 20% No 03/10/2025
Formal and Observed Learning: Exam 60% No Examination period

Formal and Observed Learning: Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 20 hours
Due: 15/09/2025
Weighting: 20%

 

The purpose of this assessment is for you to demonstrate your understanding of statistical learning concepts, focusing on the assessment of model accuracy, decision tree models (including ensemble methods), principal component analysis (PCA), and clustering methods.

You will participate in a test (approximately 90 minutes) held during class time to perform calculations, analyses, and interpretations related to topics learnt in the semester.

Skills in focus:

  • Discipline knowledge
  • Critical thinking and problem solving

Deliverable: Class test demonstrating responses to problem scenarios and discipline related questions.

Individual Assessment

 
On successful completion you will be able to:
  • Demonstrate an understanding of the various concepts related to the assessment of model accuracy in statistical learning.
  • Explain the basics and key concepts associated with decision tree models and its ensemble methods and perform related interpretations.
  • Explain the uses and key concepts of principal component analysis and perform related interpretations.

Professional Practice: Risk Modelling

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 10 hours
Due: 03/10/2025
Weighting: 20%

 

The purpose of this assessment is for you to demonstrate problem-solving skills in R, focusing on model accuracy assessment, decision trees (including ensemble methods), principal component analysis, and clustering techniques. You will apply these concepts in R and compare their effectiveness in different scenarios.

You will complete problem-solving questions requiring detailed solutions in R.

Skills in focus:

  • Critical thinking and Problem solving
  • Digital skills
  • Communication
  • Discipline knowledge

Deliverable: Written solutions (with R code).

Individual assessment

 
On successful completion you will be able to:
  • Demonstrate an understanding of the various concepts related to the assessment of model accuracy in statistical learning.
  • Explain the basics and key concepts associated with decision tree models and its ensemble methods and perform related interpretations.

Formal and Observed Learning: Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: Examination period
Weighting: 60%

 

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: Formal exam.

Individual assessment

 
On successful completion you will be able to:
  • Demonstrate an understanding of the various concepts related to the assessment of model accuracy in statistical learning.
  • Explain the basics and key concepts associated with decision tree models and its ensemble methods and perform related interpretations.
  • Explain the uses and key concepts of principal component analysis and perform related interpretations.
  • Demonstrate an understanding of the various concepts related to clustering methods.

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

Delivery and Resources

Please refer to iLearn for details.

Unit Schedule

Week Topic Assessments
1 Degrees of Freedom and Bias–Variance Decomposition  
2 Leave-One-Out Statistics and Studentized Residuals  
3 Reparameterization and Successive Orthogonalisation  
4 Introduction to Decision Trees  
5 Classification Trees and Node Impurity Measures  
6 Ensemble Trees and Bagging  
7 Random Forests  
8 Boosting and Exponential Loss in Classification Trees  Class test
— Study Break Assignment
9 Principal Component Analysis  
10 PCA as Closest Hyperplanes and Applications  
11 K-Means Clustering  
12 Hierarchical Clustering  
13 Revision  

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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.

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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.

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

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Unit information based on version 2025.04 of the Handbook