Course coordinator
Dr Morteza Namvar
Available for zoom meetings (weeks 1-4)�by appointment
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Machine learning concerns the use of learning algorithms to extract patterns and insights from data. Students will develop effective machine learning models that inform or automate business decisions and actions. This involves practical knowledge and experience in selecting and preparing data, training and evaluating advanced models and fine-tuning the parameters of learning algorithms based on the business problem at hand. During the course, students will learn to translate the insights generated by algorithms to solve real-world business problems.
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. With the proliferation of Web 2.0 and social media, the availability of text data is greater than ever, and it is crucial for a business to understand, analyse and interpret all the available data sources and make informed, data-driven decisions using ML.
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 predictive modelling techniques. We use Python to develop and test advanced predictive and descriptive models.
In particular, this course covers techniques and skills related to 1) data manipulation and preparation, 2) data exploration 3) classification 4) clustering and 5) text analytics. Prior knowledge of basic data analysis from BSAN7204 (Statistical Learning) and BSAN7205 (Business Analytics Foundations) are essential for successfully completing this course. The prior programming skill is not required.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BSAN7204 + 7206
Incompatible
You can't enrol in this course if you've already completed the following:
BISM3206
Restrictions
Restricted to students enrolled in the MBusAn
Course contact
Course staff
Lecturer
Dr Morteza Namvar
Guest lecturer
Dr Teerapong Leelanupab
Tutor
Mr James Boyce
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 emailﾠbusiness.mytimetable@uq.edu.auﾠfrom 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.
Develop machine learning solutions for a specific business
LO2.
Critically evaluate the applicability of machine learning solutions
LO3.
Communicate and justify machine learning projects to business and technical audiences
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Quiz |
ML Mastery Quizzes
|
20% |
Week 2 - Week 10
Quizzes must be completed online within the designated timeframe during the relevant week. More details will be available on Ultra as the semester begins. |
| Presentation |
Oral Examination
|
30% |
Week 11 - Week 12 |
| Computer Code, Project |
Jupyter Lifecycle Mastery in ML Project
|
50% |
31/10/2025 1:00 pm |
Assessment details
ML Mastery Quizzes
- Online
- Mode
- Written
- Category
- Computer Code, Quiz
- Weight
- 20%
- Due date
Week 2 - Week 10
Quizzes must be completed online within the designated timeframe during the relevant week. More details will be available on Ultra as the semester begins.
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
Eight quizzes will be delivered during Weeks 2 to 10 of the semester. Each quiz will be available during the relevant week and is designed to assess your understanding of the weekly content and practical tasks.
Quizzes must be completed within the designated timeframe. A missed quiz will result in a score of zero.
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
Online quizzes will be delivered during Weeks 2 to 10 of the semester. Each quiz is based on the weekly bootcamp activity and is designed to assess your understanding of the practical tasks and concepts covered that week.
Deferral or extension
You may be able to apply for an extension.
Oral Examination
- Online
- Mode
- Oral
- Category
- Presentation
- Weight
- 30%
- Due date
Week 11 - Week 12
- Learning outcomes
- L01, L02, L03
Task description
During Weeks 11 and 12, students will deliver an individual oral presentation outlining the progress of their ML project. This assessment is designed to evaluate your understanding of the project, your approach to data preparation and modelling, and your ability to communicate key insights.
You will receive constructive feedback during this session, which can be used to enhance your final project submission. The oral presentation contributes 30% to your overall project grade.
Submission guidelines
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.
Jupyter Lifecycle Mastery in ML Project
- Online
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Project
- Weight
- 50%
- Due date
31/10/2025 1:00 pm
- Learning outcomes
- L01, L02
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.
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.
Additional learning resources information
Our course will use Open Source Software (OSS),ﾠYou can download designated softwares into own or lab computers freely
Python (https://www.anaconda.com/distribution/#downloadsection)ﾠ
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 |
Module 1.1: Introduction to ML and Python An introduction to Machine Learning concepts and Python as a tool for ML. Live session: Staff and student introductions, course objectives, assessments overview, and discussion of ML fundamentals. Self-directed learning: Complete introductory materials before the session. Learning outcomes: L01, L03 |
Tutorial |
Module 1.1 Coding Bootcamp Introduction to Python programming and Jupyter Notebook setup. Learning outcomes: L01 |
|
Week 2 |
Lecture |
Module 1.2: Python coding for ML Hands-on session covering Python data structures, libraries (pandas, numpy), and basics of coding for ML. Live session: Guided coding demonstrations and exercises. Self-directed learning: Review Python examples and practice exercises. Learning outcomes: L01 |
Tutorial |
Module 1.2: Coding Bootcamp Practicing Data Types, Loops, and Conditionals in Python Learning outcomes: L01 |
|
Week 3 |
Lecture |
Module 2.1: Cluster Analysis Exploration of unsupervised learning through clustering methods. Live session: Theory and examples of k-means, hierarchical clustering, and evaluation metrics. Self-directed learning: Study clustering case studies. Learning outcomes: L01, L02, L03 |
Tutorial |
Module 2.1: Coding Bootcamp Clustering datasets and visualizing results. Learning outcomes: L01 |
|
Week 4 |
Lecture |
Module 2.2: Classification Introduction to supervised classification methods (e.g., decision trees and Random Forest). Live session: Classification theory, use cases, and demonstrations. Self-directed learning: Readings and exercises on classification. Learning outcomes: L02, L03 |
Tutorial |
Module 2.2: Coding Bootcamp Building Decision Tree classifiers in Python. Learning outcomes: L01 |
|
Week 5 |
Lecture |
Module 2.3: Classification Evaluation Focus on evaluating classification models. Live session: Confusion matrix, accuracy, precision, recall, ROC, and AUC. Self-directed learning: Review examples of evaluation techniques. Learning outcomes: L02, L03 |
Tutorial |
Module 2.3: Coding Bootcamp Calculating and interpreting evaluation metrics. Learning outcomes: L01 |
|
Week 6 |
Lecture |
Module 2.4: Other Classification Techniques Exploring additional classifiers like SVM, k-NN, and Naive Bayes. Live session: Discussion of alternative approaches and their pros & cons. Self-directed learning: Study supplementary materials on advanced classifiers. Learning outcomes: L02, L03 |
Tutorial |
Module 2.4: Coding Bootcamp Implementing and comparing advanced classification methods. Learning outcomes: L01 |
|
Week 7 |
Lecture |
Module 3.1: Text Preparation for ML Working with unstructured text data for ML. Live session: Text cleaning, tokenization, and feature extraction techniques. Self-directed learning: Readings and exercises on text preprocessing. Learning outcomes: L02, L03 |
Tutorial |
Module 3.1: Coding Bootcamp Text preprocessing in Python. Learning outcomes: L01 |
|
Week 8 |
Lecture |
Module 3.2: Text-Driven ML Applying ML techniques to text data. Live session: Sentiment analysis, text classification, and business use cases. Self-directed learning: Review case studies in Text-Driven ML. Learning outcomes: L02, L03 |
Tutorial |
Module 3.2: Coding Bootcamp Building models for sentiment and text classification. Learning outcomes: L01 |
|
Week 9 |
Lecture |
Module 3.3: Recommender Systems Designing and implementing recommendation algorithms. Live session: Collaborative and content-based filtering, hybrid approaches. Self-directed learning: Study examples of recommender systems in practice. Learning outcomes: L02, L03 |
Tutorial |
Module 3.3: Coding Bootcamp Students work on their ML project and may seek support during this session. Learning outcomes: L01 |
|
Mid Sem break |
Not Timetabled |
In-semester break |
Week 10 |
Lecture |
Module 4.1: Artificial Neural Networks (ANN) Understanding the fundamentals of ANNs and their components. Live session: Neurons, layers, activation functions, and backpropagation. Self-directed learning: Readings on ANN theory and applications. Learning outcomes: L02, L03 |
Tutorial |
Module 4.1: Coding Bootcamp Implementing simple neural networks with Python. Learning outcomes: L01 |
|
Week 11 |
Lecture |
Module 4.2: Deep Learning (DL) Understanding advanced deep learning architectures, including CNNs and RNNs, and their applications to image and sequence data. Live session: Oral presentations + Introduction to CNNs and RNNs in DL. Self-directed learning: Incorporate feedback and explore DL architectures. Learning outcomes: L02, L03 |
Tutorial |
Module 4.2: Oral Examination Students present progress on their final ML projects and receive feedback. Learning outcomes: L01, L02, L03 |
|
Week 12 |
Lecture |
Module 4.3: Introduction to LLMs Exploring Large Language Models and their foundations. Live session: Transformers, pre-training/fine-tuning, attention mechanisms. Self-directed learning: Review LLM resources and examples. Learning outcomes: L03 |
Tutorial |
Module 4.3: Assignment support Students receive help finalizing their ML projects. Learning outcomes: L01, L02, L03 |
|
Week 13 |
Lecture |
Module 4.4: LLMs’ Applications & Course Wrap-Up Examining real-world applications of LLMs and reflecting on course learning. Live session: Student Q&A, final thoughts, and feedback session. Self-directed learning: Submit final project and review key learnings. Learning outcomes: L03 |
Tutorial |
Module 4.4: Assignment support Final drop-in session to assist with project submission. Learning outcomes: L01, L02, L03 |
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.