Course coordinator
Dr Zara Taba
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
- Business School
Using R, this course introduces students to the family of models that define the field of predictive analytics. Specific model forms introduced in the course include linear and logistic regression, neural networks, and recommendation systems. The focus is on developing an intuition for predictive modelling and knowledge of the essential concepts.
Course requirements
Assumed background
Students should have completed the course BSAN2204 Methods of Business Analytics before enrolling in the course BSAN2205 Machine Learning for Business (per the prerequisites).
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM2204 or BSAN2204
Incompatible
You can't enrol in this course if you've already completed the following:
BISM2205
Course contact
Course staff
Lecturer
Dr Zara Taba
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full name,
- Student ID, and
- the Course Code
Aims and outcomes
The broad aim of the course is to provide students with an indepth exposure to the methods of machine learning as they apply to business. Emphasis is placed on the established methods of machine learning, including tree-based methods and support vector machines. The course is applied in nature and makes extensive uses of R, which is open-source software for numerical analysis. The course is organised into four modules: preparation for machine learning, regression techniques for prediction and classification, machine learning techniques for prediction and classification, and machine learning methods for clustering. The more general goals of the course are to learn the language of machine learning, develop understanding of the key concepts in the application of machine learning, to develop appreciation of the application of machine learning business, and to develop an awareness of the policy and strategy implications of machine learning for business and beyond. The course builds on BSAN2204 Methods of Business Analytics and provides a context in which students can further develop their understanding of the R environment with a view towards becoming effective and professional business analysts.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Recognise and explain the role of machine learning for business.
LO2.
Explain the key concepts in machine learning for business.
LO3.
Apply the methods of machine learning to business.
LO4.
Compare and critically evaluate the methods of machine learning.
LO5.
Demonstrate how applications of machine learning can inform and improve business performance.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project |
A1 - Project Proposal
|
20% |
2/09/2024 5:00 pm |
| Computer Code, Paper/ Report/ Annotation, Presentation, Project |
A2 - Project Report and Presentation
|
50% |
21/10/2024 5:00 pm |
| Computer Code, Practical/ Demonstration, Tutorial/ Problem Set |
A3 - R Based Analytical Take Home Exam
|
30% |
11/11/2024 - 15/11/2024 |
Assessment details
A1 - Project Proposal
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
2/09/2024 5:00 pm
- Learning outcomes
- L01, L02, L04
Task description
This first assessment item is a project proposal outlining the application of a method of predictive analytics to business. The proposal is an individual and written assessment item.
Students will write proposals in the range of 1,500 to 2,000. The proposal should contain several key components.
1) Background
2) Problem definition
3) Proposed approach/method of analysis
4) Data/information requirements
5) Analysis plan
6) Project directions
More details of criteria and suggestions for approaching each section of the proposal will be discussed in class and provided in written form on Blackboard. Note students are expected to submit their proposals in Word format.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task.. Students must clearly reference any use of AI in each instance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit through Blackboard.
Deferral or extension
You may be able to apply for an extension.
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.
A2 - Project Report and Presentation
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Computer Code, Paper/ Report/ Annotation, Presentation, Project
- Weight
- 50%
- Due date
21/10/2024 5:00 pm
- Learning outcomes
- L01, L02, L04, L05
Task description
This second assessment item is a project report documenting the application of a method of predictive analytics to business. The report is an individual and written assessment item, and should follow-on from the project proposal. Students will write reports in the range of 3,000 to 3,500. The report should contain several key components.
1) Background/problem definition
3) Proposed approach/method of analysis
4) Data/information requirements
5) Analysis plan/modelling approach
6) Data screening/testing assumptions
7) Results and discussion/interpretations
8) Summary of key findings
9) Project summary
More details of criteria and suggestions for approaching each section of the report will be discussed in class and provided in written form on Blackboard. Note students are expected to submit their proposals in PowerPoint format and further, record and submit a recording of their presentations.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task. Students must clearly reference any use of AI in each instance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit through Blackboard.
Deferral or extension
You may be able to apply for an extension.
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.
A3 - R Based Analytical Take Home Exam
- Online
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Practical/ Demonstration, Tutorial/ Problem Set
- Weight
- 30%
- Due date
11/11/2024 - 15/11/2024
- Learning outcomes
- L03, L05
Task description
This third assignment item is a School-based take-home assessment will run during the final exam period. Expectations for the assessment will be discussed in class. Briefly, the assessment will consistent of short-answer style questions focused on the core content of the course and process questions that require students to run analyses in R, and report and interpret the results of their R analysis.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task. Students must clearly reference any use of AI in each instance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
All assessment is to be submitted through Blackboard , detailed submission instructions will be provided on the course Blackboard Course Site.
Deferral or extension
You may be able to apply for an extension.
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
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
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 |
Seminar |
1. Course Introduction and A1 Overview Learning outcomes: L01, L02, L03, L04, L05 |
Week 2 |
Seminar |
2. Preparation for Machine Learning Learning outcomes: L01, L02, L03, L04, L05 |
Week 3 |
Seminar |
3. Linear Regression Learning outcomes: L01, L02, L03, L04, L05 |
Week 4 |
Seminar |
4. Logistic Regression Learning outcomes: L01, L02, L03, L04, L05 |
Week 5 |
Seminar |
5. Time Series Analysis Learning outcomes: L01, L02, L03, L04, L05 |
Week 6 |
Seminar |
6. Tree-Based Methods Learning outcomes: L01, L02, L03, L04, L05 |
Week 7 |
Seminar |
7. Support Vector Machines and A2 Overview Learning outcomes: L01, L02, L03, L04, L05 |
Week 8 |
Seminar |
8. Ensemble Methods Learning outcomes: L01, L02, L03, L04, L05 |
Week 9 |
Seminar |
9. Clustering Learning outcomes: L01, L02, L03, L04, L05 |
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break Learning outcomes: L01, L02, L03, L04, L05 |
Week 10 |
Seminar |
10. Project Workshop Learning outcomes: L01, L02, L03, L04, L05 |
Week 11 |
Seminar |
11. Association Rule Learning Learning outcomes: L01, L02, L03, L04, L05 |
Week 12 |
Seminar |
12. Collaborative Filtering Learning outcomes: L01, L02, L03, L04, L05 |
Week 13 |
Seminar |
13. Course Revision and A3 Preparation 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.