Unit rationale, description and aim
Machine learning is the process of teaching a machine to learn from datasets for a variety of tasks. Machine learning models and algorithms are widely used in human’s digital life such as email client, search engine, social media, virtual personal assistant, healthcare and recommendation system. This unit provides a practical and technical introduction to machine learning models and algorithms. Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are hands-on implementation and usage of various types of machine learning techniques via Python Scikit-Learn to solve real-world problems such as in digital health. This unit will also consider the issue of machine bias and how it may have an adverse impact on the common good. The aim of this unit is to equip students with foundational knowledge and practical skills in machine learning to develop ethical and effective data-driven solutions.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Demonstrate comprehensive knowledge of using data ...
Learning Outcome 01
Appraise the use of fundamental data science and m...
Learning Outcome 02
Develop an end-to-end data science and machine lea...
Learning Outcome 03
Examine the issue of machine bias and how it may a...
Learning Outcome 04
Content
Topics will include:
- Overview of data science and its implementation life cycle and tools
- Recap of data processing concepts including data quality and data operations such as cleaning, integration, reduction and transformation.
- Theory and practice of essential statistics in data science
- Machine learning (ML) introduction
- ML projects and basic linear algebra
- Basic matrix analysis and SVD, PCA
- Basic classification and evaluation with ROC curves
- Probability Theory and Naïve Bayesian Classifier
- Regression (linear, polynomial), overfitting and regularization, Bayesian regression
- Clustering: k-means and mixture of Gaussians
- Better evaluation with k-fold cross validation and finetune model with grid search
- Machine bias in the real world and its impact on the common good
Assessment strategy and rationale
A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. The first assessment consists of a series of fortnightly programming tasks to practice data processing, analysis practical and machine learning skills. The purpose is to assess students’ practical skills of using Python data science and machine learning libraries and tools for data processing and analysis. The second assessment is a more specific image data exploration and machine learning preparation task that covers fundamental knowledge of data science and machine learning. The purpose is to assess students’ understanding and skills in data preparation for machine learning preparation. The final assessment is a group project to do experiments with machine learning models and algorithms. The purpose is to assess students’ knowledge and skills of applying key machine learning algorithms to solve real-world problems e.g. in digital health with consideration of machine bias, continuing from the machine learning preparation task.
The assessments for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, students are required to obtain an overall mark of at least 50%.
Overview of assessments
Assessment Task 1: Lab practical The first assess...
Assessment Task 1: Lab practical
The first assessment item consists of practicing simple Python data science and machine learning libraries and tools. The assessment requires students to demonstrate their understanding and use of Python data science and machine learning libraries and tools for small sized tasks.
Submission Type: Individual
Assessment Method: Content knowledge coding task
Artefact: Code
30%
Assessment Task 2: Data exploration tasks prepari...
Assessment Task 2: Data exploration tasks preparing for Machine learning project
The second assessment is to prepare specific image data for machine learning models and algorithms exploration. The purpose is to assess students’ understanding and skills in using Python data science and machine learning packages in data exploration.
Submission Type: Individual
Assessment Method: Conceptual knowledge coding tasks
Artefact: Code
30%
Assessment Task 3: Machine learning project The f...
Assessment Task 3: Machine learning project
The final assessment is a group-based machine learning assignment focusing on machine learning models and algorithms to solve real-world problems such as in digital health. The assessment requires students to develop an end-to-end machine learning project with key machine learning algorithms and consideration of machine bias.
Submission Type: Group
Assessment Method: Applying knowledge project task
Artefact: Code and Report
40%
Learning and teaching strategy and rationale
This unit adopts a blended multimode delivery, grounded in constructivist and experiential learning theories, which emphasise active engagement, contextualised learning, and the integration of theory and practice. Students construct knowledge through guided exploration, peer interaction, and hands-on activities that mirror real-world applications of machine learning.
In the multimode offering, students engage over a twelve-week semester (or equivalent), accessing all core learning materials, formative and summative assessments online via Canvas. While there are no traditional lectures, students are required to attend a weekly two-hour workshop and a fortnightly one-hour lab session. Workshops support the development of conceptual understanding and problem-solving skills, while labs focus on practical application using tools such as Scikit-Learn. This structure ensures alignment between learning outcomes and professional skill development.
Students should expect to commit approximately 150 hours of study, including attendance, reading, online activities, and assessment preparation.
For ACU Online, an active learning approach fosters flexible and student-centred engagement. Asynchronous discussions, practical activities, and regular feedback help learners apply concepts to real-life contexts, share examples from their own experience, and interact meaningfully with peers. The design supports both independent and collaborative learning in a fully online environment.