Unit rationale, description and aim
Machine learning involves enabling computational systems to learn from data and perform a wide range of tasks without being explicitly programmed. These models and algorithms underpin many aspects of contemporary digital life, including email filtering, search engines, social media platforms, virtual assistants, healthcare applications, and recommendation systems.
This unit offers a technical and practice‑oriented introduction to core machine learning models and algorithms. Key topics include pattern discovery, classification, regression, feature extraction, and data visualisation. Students will also engage in hands‑on implementation using Python and the Scikit‑Learn library to address real‑world challenges, including applications in digital health.
The unit further examines issues of machine bias and the potential consequences of inequitable or harmful algorithmic outcomes. Overall, the unit aims to equip students with foundational knowledge and practical skills to design ethical, effective, and responsible
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.
Apply data science libraries and tools to process ...
Learning Outcome 01
Critically evaluate the use of fundamental data sc...
Learning Outcome 02
Develop effective end-to-end data science and mach...
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 project involving the design and execution of experiments using 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 requires students to apply Python-based data science and machine learning libraries and tools to a series of small tasks, demonstrating both their understanding and practical proficiency. The assessment requires students to demonstrate their understanding and use of Python data science and machine learning libraries and tools for small sized tasks.
Students use Generative AI tools for coding and reflect on their effectiveness and limitations.
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:
Computer Code + Presentation + Online Viva
Artefact: Code + Live/Recorded Presentation with Face overlay (5 minutes) + Online Viva
30%
Assessment Task 3: Machine learning project The ...
Assessment Task 3: Machine learning project
The final assessment is a 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: Individual
Assessment Method: Coding + Presentation
Artefact: Code + Live/Recorded Presentation with Face overlay (8 minutes) + Online Viva
40%
Learning and teaching strategy and rationale
This unit is delivered through Attendance and Online modes using a single, integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Across both modes, learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face classes at designated locations, supported by preparatory activities prior to workshops and opportunities for consolidation following classes. Online learning platforms are used to complement face-to-face teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts.
Across both delivery modes, students should plan to commit approximately 150 hours to this unit over the semester, including participation in learning activities, independent study, readings and assessment preparation.