Course coordinator
Dr Morteza Namvar
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
This course is an advanced offering focused on ways in which organisations can utilise machine learning (ML) in practice to gain insights and make better decisions. Through utilising cutting-edge ML techniques, students will develop analytics skills designed to tackle business challenges.
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-the-art techniques and develop critical skills to address current business challenges in today’s data-rich environments. The course emphasizes a hands-on approach, focusing on the practical application of ML to improve business decision-making. Students will analyze real-world business data using advanced ML techniques, with a special focus on the advantages of text data in developing ML models. Additionally, an introduction to large language models (LLMs) will be provided. Python software will be used to develop and test advanced ML 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 LLMs
In addition to these techniques, the course will provide business insights from several market leaders, offering a practical context for the concepts covered.
Course requirements
Assumed background
Prior knowledge of basicﾠdata analysis from BISM2202 (Data Analytics and Information Management) 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:
BISM2202 OR 8 units of BInfTech courses
Incompatible
You can't enrol in this course if you've already completed the following:
BISM7217 or BSAN7212 or MGTS3206 or MGTS7217
Restrictions
Quota: Minimum of 35 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.
Build your machine learning (ML) capabilities to use data for innovative business solutions
LO2.
Enhance knowledge and skills in the current trends in the management and use of ML
LO3.
Differentiate, design and assess various ML and data mining 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 |
|---|---|---|---|
| Quiz |
ML Quizzes
|
15% |
Week 2 Mon - Week 8 Fri
During Tutorials |
| Examination |
ML Foundation In-Semester Exam
|
40% |
In-semester Saturday 29/03/2025 - 12/04/2025 |
| Computer Code, Project | ML Project: Jupyter Lifecycle Expedition | 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 during your assigned tutorial. |
Assessment details
ML Quizzes
- In-person
- Mode
- Activity/ Performance
- Category
- Quiz
- Weight
- 15%
- Due date
Week 2 Mon - Week 8 Fri
During Tutorials
- Learning outcomes
- L01, L02, L03
Task description
There will be 7 quizzes throughout the semester, conducted during tutorials in weeks 2, 3, 4, 5, 6, 7, and 8.
To participate in a quiz, you must be present in your tutorial during the scheduled week. A missed quiz will receive a mark of zero.
Your final quiz grade will be based on your best 5 marks out of the 7 quizzes. This allows you to skip two quizzes without affecting your final grade in this assessment component.
Note: Students enrolled in Friday tutorials will have an alternative assessment for Week 8 (Quiz 7) due to the public holiday on April 18th.
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.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
There are no extensions due to the weekly quiz structure of best 5 of 7 Quizzes. A missed quiz will give a mark of 0.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
ML Foundation In-Semester Exam
- 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, L04, L05
Task description
The ML Foundation 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 ML concepts.
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.
ML Project: Jupyter Lifecycle Expedition
- 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 during your assigned tutorial.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
This individual assignment offers a comprehensive opportunity for hands-on engagement in 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: L02 |
Week 2 |
Lecture |
Python coding for ML Learning outcomes: L01, L02 |
Tutorial |
Data Types, Loops, and Conditionals in Python + Quiz 1 Learning outcomes: L01 |
|
Week 3 |
Lecture |
Cluster analysis Learning outcomes: L01 |
Tutorial |
k-Means in Python + Quiz 2 Learning outcomes: L01, L02 |
|
Week 4 |
Lecture |
Classification Learning outcomes: L01, L03, L04, L05 |
Tutorial |
Basic classifiers in Python + Quiz 3 Learning outcomes: L01, L03, L04 |
|
Week 5 |
Lecture |
Classification evaluation Learning outcomes: L01, L03, L05 |
Tutorial |
Cross Validation in Python + Quiz 4 Learning outcomes: L01, 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: L01, L03, L04, L05 |
Tutorial |
Text processing in Python + Quiz 6 Learning outcomes: L01, 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 Learning outcomes: L01, L02 |
Tutorial |
Backpropagation in Python Learning outcomes: L01, L02 |
|
Week 11 |
Lecture |
Deep Learning (DL) Learning outcomes: L01, L02, L03 |
Tutorial |
DL implementation Learning outcomes: L01, L02, L04 |
|
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, L04 |
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.