Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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
Tutorial Preferencing: Please refer to My Timetable (available via your my.UQ dashboard) for more information on the tutorial preferencing and allocation process.ﾠTutorials commence in Teaching Week 2.
The timetable is published through the UQ Public Timetable found in the APPs section of myUQ. Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information.
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 29 September. Semester 2 classes recommence Mon 30 September.
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 |
|---|---|---|---|
| Tutorial/ Problem Set | Assignment 1 | 34% |
23/08/2024 1:00 pm |
| Tutorial/ Problem Set | Assignment 2 | 33% |
20/09/2024 1:00 pm |
| Tutorial/ Problem Set | Assignment 3 | 33% |
25/10/2024 1:00 pm |
Assessment details
Assignment 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 34%
- Due date
23/08/2024 1:00 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 each question solution and computer output need to be provided.
Artificial Intelligence (AI) and Machine Translation (MT) provides 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.
Assignment 2
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
20/09/2024 1:00 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 each question solution and computer output need to be provided.
Artificial Intelligence (AI) and Machine Translation (MT) provides 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.
Assignment 3
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 33%
- Due date
25/10/2024 1:00 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 each question solution and computer output need to be provided.
Artificial Intelligence (AI) and Machine Translation (MT) provides 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
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies & Guidelines. Please refer to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIMﾠif you have not already done so.
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.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (22 Jul - 28 Jul) |
Lecture |
Basics of machine learning Introduction to the framework of statistical learning Learning outcomes: L01, L02, L03 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
S.L. 1 Introduction to fundamental aspects of Supervised Learning. Learning outcomes: L01, L02, L03 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
S.L. 2 Supervised Learning: Classification and Regression models 1. Learning outcomes: L02, L03, L04, L05 |
Week 4 (12 Aug - 18 Aug) |
No student involvement (Breaks, information) |
Public Holiday: EKKA - no lecture on Wednesday |
Week 5 (19 Aug - 25 Aug) |
Lecture |
S.L. 3 Supervised Learning: Classification and Regression models 2 Learning outcomes: L03, L04, L05 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
S.L. and R.S. 1 Supervised Learning: Further inferential aspects 1 Learning outcomes: L02, L04, L05 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
S.L and R.S. 2 Supervised Learning: Further inferential aspects 2 Learning outcomes: L02, L04, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
S.L and H.D. 1 Supervised Learning: Extensions 1 Learning outcomes: L02, L04, L05 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
S.L and H.D. 2 Supervised Learning: Extensions 2 Learning outcomes: L02, L04, L05 |
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid-Semester Break No Classes during the break. |
Week 10 (30 Sep - 06 Oct) |
Lecture |
S.L. 5 Supervised Learning: Further topics Learning outcomes: L02, L04, L05 |
Week 11 (07 Oct - 13 Oct) |
Lecture |
S.L. 6 Supervised Learning: Further topics Learning outcomes: L02, L04, L05 |
No student involvement (Breaks, information) |
Public Holiday: King's Birthday - no tutorial on Monday Students who usually attend a Monday tutorial session are advised to attend another tutorial session for this week only. |
|
Week 12 (14 Oct - 20 Oct) |
Lecture |
S.L. 7 Supervised Learning: Further topics Learning outcomes: L02, L04, L05 |
Week 13 (21 Oct - 27 Oct) |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.