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Machine Learning and Security (COMP90098)
Graduate courseworkPoints: 12.5Not available in 2026
About this subject
Overview
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Please note: this subject is delivered wholly online and only open to students enrolled in the wholly online Master of Cyber Security (MC-CYBSCMO). Subjects in this course are delivered in an online accelerated learning model and therefore, students typically enrol in one 12.5 credit point subject per online teaching term.
This subject integrates the study of Machine Learning and Security Analytics to address the growing challenges of data protection in our increasingly data-dependent world.
Students will learn the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms, and data structures, alongside practical data analysis skills. Simultaneously, the course will explore how to protect data and automate its analysis to detect, predict, and prevent privacy and security vulnerabilities, leveraging machine learning techniques to manage the sheer volume and complexity of modern data and security threats.
Intended learning outcomes
On completion of this subject, students should be able to:
- apply elementary mathematical concepts used in machine learning to complex theoretical and practical situations;
- design, implement, and evaluate machine learning systems for real-world problems;
- synthesise and implement a comprehensive range of pattern recognition and machine learning algorithms for applications in security analytics;
- critically evaluate and select appropriate computational techniques for security analytics to solve real‐world problems, based on their accuracy and efficiency for the given context;
- discuss theoretical challenges and emerging trends for security analytics and their implications in real-world scenarios.
Generic skills
On completion of this subject, students should have developed the following generic skills:
- develop skills to utilise a systems approach to complex problems;
- ability to undertake problem identification, formulation and solution;
- ability to utilise a systems approach to complex problems;
- capacity for creativity and innovation;
- ability to communicate the results of complex analysis effectively to both technical audiences and the community at large.
Last updated: 7 November 2025