Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
As bigger datasets become available and more and more companies and institutions require analysis of such a huge amount of information, the fields of Big Data and Machine Learning become more and more essential for economics and business students to learn about. This course builds on the basic knowledge built in elementary econometrics courses and strives to provide basic tools for analysing Big Data. The major topics discussed will be supervised learning (linear regression in high dimensions, classification by logistic regression and support vector machines, splines, nearest neighbours), unsupervised learning and Neural Networks. The course will be practical and will provide students with an R library of computer code to explore the topics in a practical fashion.
The course will introduce several machine learning techniques and models at an introductory level. The course is practically oriented as everything will be illustrated on datasets using the R computing framework.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1310
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON2300
Course contact
School enquiries
Student Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1.
Tutorials commence in Week 1.
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
- Public Holidays: Wed 13 August (Royal Queensland Show Holiday), Mon 6 October (King’s Birthday public holiday).
- Mid-Semester Break: 29 September - 3 October. Semester 2 classes recommence on Tuesday, 7 October.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up-to-date information, as from time to time, room changes may occur.
Aims and outcomes
The aim of the course is to get an introduction to some major machine learning techniques and models (both supervised/unsupervised, parametric/non-parametric, regression/Classification) and how to apply to real data sets using the R computing environment.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand the concept of statistical learning.
LO2.
Learn how to use the R computing framework.
LO3.
Understand the difference between supervised and unsupervised learning.
LO4.
Learn about some major classification techniques in machine learning.
LO5.
Learn about some major regression techniques in machine learning.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Quiz, Tutorial/ Problem Set | Problem Solving, Data Analysis and Short Report | 40% (Best 4 of 6) |
Periodic Assessments Throughout the Semester |
| Examination |
In-semester Exam
|
30% |
22/09/2025 8:00 am
The exam will take place during the regular lecture time. |
| Computer Code, Project | Final Assessment | 30% |
31/10/2025 4:00 pm |
Assessment details
Problem Solving, Data Analysis and Short Report
- Mode
- Written
- Category
- Computer Code, Quiz, Tutorial/ Problem Set
- Weight
- 40% (Best 4 of 6)
- Due date
Periodic Assessments Throughout the Semester
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Take-home quizzes throughout the semester, with the first assignment due on Friday in Week 4. There will be a total of 6 quizzes.
Each quiz will consist of short-answer questions and R-exercises related to the material covered in lectures and tutorials. Students are strictly required to use R-markdown to generate a PDF file and submit it online via Blackboard.
Marks will be awarded as indicated for each question on the quiz. The highest 4 scores across 6 quizzes will be counted towards a student's final grade for the course. Students will be given the opportunity to obtain partial marks for incorrect final answers by explaining the steps taken in the derivations and clearly identifying where the error occurred.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online via Blackboard.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
No extensions are possible, even with a medical certificate. Online Turnitin access will be blocked after the due date and time.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
No late submissions will be accepted. No extensions are possible, even with a medical certificate. Online Turnitin access will be blocked after the due date and time.
In-semester Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
22/09/2025 8:00 am
The exam will take place during the regular lecture time.
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
- This is a closed-book exam to be sat in a designated examination room at the St Lucia campus. The exam will cover statistical learning methods up to week 8.
Marks will be shown on the paper next to each question. Up to half marks will be awarded for incorrect numerical answers, provided correct methods/formulas have been used.
This assessment task provides a verifiable assessment of the students' abilities, skills and knowledge.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Final Assessment
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 30%
- Due date
31/10/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignment will contain a set of practical and theoretical questions and cover topics from previous lectures and tutorials. Answers, including the solution to each question and the computer output, need to be provided. Students are strictly required to use R-markdown to generate a PDF file and submit it online via Blackboard.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Online submission via BB.
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.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
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.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student's final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
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.
Additional learning resources information
Install both R and RStudio on your laptop. See the instructions on the RStudio website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (28 Jul - 03 Aug) |
Lecture |
Basics of machine learning Introduction to the Framework of Statistical Learning Learning outcomes: L01, L02, L03 |
Week 2 (04 Aug - 10 Aug) |
Lecture |
S.L. 1 Statistical Learning: Classification and Regression Models 1. Learning outcomes: L01, L02, L03 |
Week 3 (11 Aug - 17 Aug) |
Lecture |
S.L. 2 Statistical Learning: Classification and Regression Models 2. |
Week 4 (18 Aug - 24 Aug) |
Lecture |
S.L.3 Statistical Learning: Classification and Regression Models 3. Learning outcomes: L01, L02, L03 |
Week 5 (25 Aug - 31 Aug) |
Lecture |
S.L. 4 Statistical Learning: Classification and Regression Models 4 Learning outcomes: L03, L04, L05 |
Week 6 (01 Sep - 07 Sep) |
Lecture |
S.L. and R.S. 1 Statistical Learning: Further Inferential Aspects 1 Learning outcomes: L02, L04, L05 |
Week 7 (08 Sep - 14 Sep) |
Lecture |
S.L and R.S. 2 Statistical Learning: Further Inferential Aspects 2 Learning outcomes: L02, L04, L05 |
Week 8 (15 Sep - 21 Sep) |
Lecture |
S.L and H.D. 1 Statistical Learning: Extensions 1 Learning outcomes: L02, L04, L05 |
Week 9 (22 Sep - 28 Sep) |
Problem-based learning |
In-semester examination See Assessment section for more details. Learning outcomes: L01, L02, L03, L04, L05 |
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Semester Break No Classes during the break. |
Week 10 (06 Oct - 12 Oct) |
Tutorial |
King's Birthday - no lecture and tutorial on Monday R exercises on statistical learning methods. Students allocated to the Monday tutorial are invited to attend another tutorial for this week only. |
Week 11 (13 Oct - 19 Oct) |
Lecture |
S.L. and F.T. 1 Statistical Learning: Further Topics 1. Learning outcomes: L02, L04, L05 |
Week 12 (20 Oct - 26 Oct) |
Lecture |
S.L. and F.T 2 Statistical Learning: Further Topics 2. Learning outcomes: L02, L04, L05 |
Week 13 (27 Oct - 02 Nov) |
Lecture |
Review Review Topics Learning outcomes: L01, L02, L03, L04, L05 |
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
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.