Course coordinator
Dr Morteza Namvar
Available for zoom meetings (weeks 1-4)�by appointment
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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 Thomas Taimre
Guest lecturer
Dr Teerapong Leelanupab
Timetable
The timetable for this course is available on the UQ Public Timetable.
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, Paper/ Report/ Annotation | Classification Assignment | 50% |
13/09/2024 1:00 pm |
| Computer Code, Project | Text Analytics Assignment | 50% |
25/10/2024 1:00 pm |
Assessment details
Classification Assignment
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 50%
- Due date
13/09/2024 1:00 pm
- Learning outcomes
- L01, L02, L03
Task description
This assignment is an individual project report on a topic related to machine learning (ML). A project report is a document that presents a predictive model, its evaluation and its advantage and uses for business decision-making.
The aim is to provide experience in the steps involved with creating, evaluating, improving classification models and finally presenting and interpreting the model in a business report. You are strongly encouraged to commence this assignment by the end of the third week of the semester, and you should progress thoughtfully through the steps. Hasty decisions made early in the design process may result in much more work later.
You will be provided with a dataset, such as one about the direct marketing campaigns of a bank. You will be asked to perform specific tasks and develop related models for decision-making.
Your reports should include the following parts:
- Executive summary
- Model building
- Model evaluation
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.
Text Analytics Assignment
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Project
- Weight
- 50%
- Due date
25/10/2024 1:00 pm
- Learning outcomes
- L01, L02
Task description
This assignment is an individual assignment. The aim is to provide experience in the steps involved with creating, evaluating, improving an analytics model and finally, presenting and interpreting the model in a business report.
You are strongly encouraged to commence this assignment in week 9, and you should progress thoughtfully through the steps.
Hasty decisions made early in the design process may result in much more work later.
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; however, 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.
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 |
Seminar |
Topic 1.1 Clustering Self-Directed Learning: a) Cluster Analysis in Business, b) Partitioning Methods: K- Means, c) Partitioning Methods: k-Mediods, d) Hierarchical Cluster Analysis, and e) Quality of Clusters Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 1.1 This session provides guidelines on how to use the Jupyter notebook for cluster analysis and its evaluation. Learning outcomes: L01 |
|
Week 2 |
Not Timetabled |
Topic 1.2 Classification Self-Directed Learning: a) Introduction to Classification, b) Learning & Classification, c) Decision Tree, d) Decision Tree Induction, and e) Random Forests Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 1.2 This session provides guidelines on how to use Jupyter notebook for developing a classifier using Decision Tree and Random Forest Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Topic 1.2 Evaluation of Classification Self-Directed Learning: a) Introduction to Evaluation, b) Evaluation Methods, c) Confusion Matrix, and d) Model Comparison Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 1.3 This session provides guidelines on how to use Jupyter notebook for developing a confusion matrix and ROC. Learning outcomes: L01 |
|
Week 4 |
Not Timetabled |
Assignment 1 working week Self-Directed Learning: Work independently to complete your first assessment. |
Week 5 |
Not Timetabled |
Topic 2.1 Naive Bayes (NB) and KNN Self-Directed Learning: a) Foundations of NB for Classification, and b) Foundations of KNN for Classification Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 2.1 This session provides guidelines on how to use the Jupyter notebook for developing NB and KNN. Learning outcomes: L01 |
|
Week 6 |
Not Timetabled |
Artificial Neural Network ANN) Self-Directed Learning: a) Introduction to ANN, and b) ANN for Classification Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 2.2 This session provides guidelines on how to use the Jupyter notebook for developing ANN. Learning outcomes: L01 |
|
Week 7 |
Not Timetabled |
Support Vector Machine (SVM) Self-Directed Learning: a) Introduction to SVM, b) SVM for Classification Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 2.3 This session provides guidelines on how to use the Jupyter notebook for developing SVM. Learning outcomes: L01 |
|
Week 8 |
Not Timetabled |
Assignment 1 working week Self-Directed Learning: Continue independent work on your assessment. |
Week 9 |
Lecture |
Topic 3.1 Text preprocessing Self-Directed Learning: a) BOW & TFIDF, and b) Dimensionality Reduction & SVD Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 3.1 This session provides guidelines on how to use import text data into Jupyter notebook and prepare it for future analysis. Learning outcomes: L01 |
|
Mid Sem break |
Not Timetabled |
In-semester break |
Week 10 |
Not Timetabled |
Topic 3.2 Model building using text Self-Directed Learning: a) Topic modelling, b) Sentiment classification, c) Document, sentence and feature level for sentiment classification, d) Sentiment classification methods (unsupervised and supervised), e) Word embedding Learning outcomes: L01, L02, L03 |
Seminar |
Coding Bootcamp 3.2 This session provides guidelines on how to use the Jupyter notebook for topic modelling and sentiment classification. Learning outcomes: L01 |
|
Week 11 |
Not Timetabled |
Advanced ML topics 1 Self-Directed Learning: a) Deep Learning, and b) Human in the Loop AI Learning outcomes: L03 |
Seminar |
Discussion 4.1 This session provides opportunities for students to discuss the applications of advanced ML technologies in different business settings. Learning outcomes: L02, L03 |
|
Week 12 |
Not Timetabled |
Advanced ML topics 2 Self-Directed Learning: a) Graph Data and Graph NN, b) NLP, and c) Deep Fakes Learning outcomes: L03 |
Seminar |
Discussion 4.2 This session provides opportunities for students to discuss the applications of Graph Data, NLP, and Deep Fakes in different business settings. Learning outcomes: L03 |
|
Week 13 |
Not Timetabled |
Assignment 2 working week Self-Directed Learning: Independent work on assessment. |
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.