Course coordinator
Dr Morteza Namvar
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
In this course, students will analyse real-world data using advanced Machine Learning (ML) techniques. The course will be hands-on, with an emphasis on technical aspects so that students learn how analysts apply ML to improve business decision-making.
This course is an advanced course concerned with machine learning (ML), which refers to the ways in which enterprises such as businesses, non-profits, and governments can use data to gain insights and make better decisions. With the increasing availability of broad and deep sources of data, ML is becoming an even more critical capability for enterprises of all types and all sizes. The ability to use data effectively to drive timely, precise and profitable decisions has been a critical strategic advantage for companies and is in high demand in the industry.
In this course, students will learn state-of-art techniques and critical skills to address existing business problems in today’s data-rich environments. The course will be hands-on and the emphasis will be placed on the know-how aspect - how to extract and apply ML to improve business decision-making. This course analyses real-world business data using advanced ML techniques. We use Python to develop and test advanced predictive and descriptive models.
In particular, this course covers techniques and skills related to:
- Coding in Python for ML
- ML foundations, including clustering and classification techniques
- ML using text
- Neural Networks, Deep Learning, and Large Language Models (LLMs)
In addition to these techniques, the course will provide business insights from several market leaders, offering a practical context for the concepts covered.
Prior programming experience is not required, as the course will begin with foundational Python skills tailored for ML applications. From there, we will delve deeper into ML concepts and techniques.
Course requirements
Assumed background
Prior knowledge of basic data analysis from BISM7233 (Data Analytics for Business) is essential for this course.
Before attempting this course, you are advised that it is important to complete the appropriate prerequisite course(s) listed on the front of this course profile. No responsibility will be accepted by UQ Business School, the Faculty of Business, Economics and Law or The University of Queensland for poor student performance occurring in courses where the appropriate prerequisite(s) has/have not been completed, for any reason whatsoever.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
8 units of MBus or MCom courses and BISM7233 or INFS7233
Incompatible
You can't enrol in this course if you've already completed the following:
BISM3206 or MGTS3206 or MGTS7217
Restrictions
Quota: Minimum of 15 enrolments
Course contact
Course staff
Lecturer
Dr Morteza Namvar
Tutor
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
- Course Code
Aims and outcomes
This course aims to train you to extract patterns in vast amounts of data and discover actionable insights and equip you with machine learning (ML) skills highly valuedﾠin the current job market. Specifically, this course has three goals. The first is to help you think critically about data and the analyses based on those data - whether conducted by you or someone else. The second is to enable you to identify opportunities for creating value using ML. The third is to help you estimate the value created using MLﾠto address an opportunity. Machine Learning is an integral part of modern management - this course should provide you with the foundation you need to understand and apply these methods to derive value.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Enhance knowledge and skills in the current trends in the management and use of machine learning (ML)
LO2.
Build your ML capabilities to use data for innovative business solutions
LO3.
Differentiate, design, and assess various ML models
LO4.
Implement efficient ML strategies to solve current business problems
LO5.
Identify and translate real-world business problems into ML models
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution, Tutorial/ Problem Set |
ML Mastery Quizzes
|
15% |
3/03/2025 - 18/04/2025
During Tutorials |
| Examination |
In Semester Exam - Essential ML
|
40% |
In-semester Saturday 29/03/2025 - 12/04/2025 |
| Computer Code, Project | Jupyter Lifecycle Mastery in ML Project | 45% |
Progress Check 1 (10% of mark) During Tutorial Week 9, Progress Check 2 (10% of mark) During Tutorial Week 12, Final Submission (90% of mark) 30/05/2025 3:00 pm
Progress Checks are during your tutorial in the allocated weeks. |
Assessment details
ML Mastery Quizzes
- In-person
- Mode
- Activity/ Performance
- Category
- Participation/ Student contribution, Tutorial/ Problem Set
- Weight
- 15%
- Due date
3/03/2025 - 18/04/2025
During Tutorials
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Seven quizzes will be given during tutorials in weeks 2-8 of the semester. Attendance in your tutorial class is mandatory for quiz participation. A missed quiz will result in a score of zero. Your final quiz grade will be based on your five highest quiz scores. This allows for two missed quizzes without affecting your final grade.
Important Note: If you are in a Friday tutorial, an alternative assessment will be provided for Week 8's quiz (Quiz 7) due to the April 18th public holiday.
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.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
There are no extensions or deferral's for this activity as the best 5 of 7 attempts are used, a missed quiz will be marked as 0.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
In Semester Exam - Essential ML
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
In-semester Saturday
29/03/2025 - 12/04/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
The Essential ML Exam thoroughly assesses your knowledge and skills in modules 1 and 2, encompassing theoretical foundations and practical implementation of machine learning (ML) techniques.
Comprising two parts, the first involves multiple-choice questions gauging your adeptness in handling data within Jupyter Notebooks, a crucial skill for robust ML model construction. It specifically evaluates your capability to import raw datasets and utilize libraries like Pandas to effectively clean and pre-process data.
The second part evaluates your comprehension of fundamental concepts, including classification, clustering, and their evaluation. Proficiency in model evaluation, hyperparameter tuning, and problem-solving is expected to be demonstrated.
The In-Semester Exam takes place during the Saturday In-Semester Exam Period.
The exam date will be confirmed when the Saturday In-Semester Exam timetable is released.
AI Statement:
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| 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.
Jupyter Lifecycle Mastery in ML Project
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Project
- Weight
- 45%
- Due date
Progress Check 1 (10% of mark) During Tutorial Week 9,
Progress Check 2 (10% of mark) During Tutorial Week 12,
Final Submission (90% of mark) 30/05/2025 3:00 pm
Progress Checks are during your tutorial in the allocated weeks.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This individual assignment provides a comprehensive opportunity for hands-on engagement with the entire lifecycle of an ML project. The primary objective is to gain practical experience in the sequential processes of conceptualizing, constructing, evaluating, refining, and ultimately presenting an ML model. The assignment is designed to enhance your proficiency in utilizing Jupyter Notebook as the exclusive platform for model development.
Throughout this project, you will delve into the intricacies of data pre-processing, model selection, and hyperparameter tuning to craft robust and effective ML models. Emphasis will be placed on understanding the iterative nature of model refinement, as you work to enhance the model's performance based on evaluation metrics.
The deliverable for this assignment is the Jupyter Notebook containing your entire workflow. This serves as a testament to your skills in model development and showcases your understanding of the practical applications of ML in a business context. This assignment aims to equip you with practical insights and skills that can be directly applied in real-world scenarios, fostering a holistic understanding of the ML project lifecycle.
Progress Checks: To receive full marks in the assignment, you are required to successfully complete two progress checks during your enrolled tutorial sessions. There is no alternative for the progress checks.
If you successfully complete the progress checks, you can earn full marks for the assignment. However, if you miss one or both progress checks, you will lose the opportunity to earn full marks.
For example, to earn full marks for the data exploration part of the assignment, your work must be deemed "very good," and you must have completed Progress Check 1. If your data exploration work meets the "very good" criteria but you have not completed Progress Check 1, you will only receive 90% of the total marks allocated for data exploration. Conversely, if you have completed Progress Check 1, you will receive 100% of the allocated marks.
AI Statement:
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
The assignment must be submitted electronically through the Blackboard Assessment link.
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 |
Lecture |
Introduction to ML and Python Learning outcomes: L01 |
Week 2 |
Lecture |
Python coding for ML Learning outcomes: L01, L02 |
Tutorial |
Data Types, Loops, and Conditionals in Python + Quiz 1 Learning outcomes: L01, L02 |
|
Week 3 |
Lecture |
Cluster analysis Learning outcomes: L02 |
Tutorial |
k-Means in Python + Quiz 2 Learning outcomes: L01, L02 |
|
Week 4 |
Lecture |
Classification Learning outcomes: L02, L03, L04, L05 |
Tutorial |
Basic classifiers in Python + Quiz 3 Learning outcomes: L02, L03, L04, L05 |
|
Week 5 |
Lecture |
Classification evaluation Learning outcomes: L02, L03, L05 |
Tutorial |
Cross Validation in Python + Quiz 4 Learning outcomes: L02, L03, L05 |
|
Week 6 |
Lecture |
Other classification techniques Learning outcomes: L03, L04 |
Tutorial |
SVM, NB and KNN in Python + Quiz 5 Learning outcomes: L03, L04 |
|
Week 7 |
Lecture |
Text preparation for ML Learning outcomes: L02, L03, L04, L05 |
Tutorial |
Text processing in Python + Quiz 6 Learning outcomes: L02, L03, L04, L05 |
|
Week 8 |
Lecture |
Text-Driven ML Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
ML using text in Python + Quiz 7 Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03, L04, L05 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Lecture |
Recommender Systems Learning outcomes: L01, L02 |
Tutorial |
Assignment Progress Check 1 Learning outcomes: L01, L02 |
|
Week 10 |
Lecture |
Artificial Neural Networks Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02 |
Tutorial |
Backpropagation in Python Learning outcomes: L01, L02 |
|
Week 11 |
Lecture |
Deep Learning (DL) Learning outcomes: L01, L02 |
Tutorial |
DL implementation Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 |
Lecture |
Introduction to LLMs Learning outcomes: L01, L02 |
Tutorial |
Assignment Progress Check 2 Learning outcomes: L01, L02 |
|
Week 13 |
Lecture |
LLMs' applications Learning outcomes: L01, L02 |
Tutorial |
LLM implementation 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.