IFQ580 Machine Learning

Machine Learning, a core discipline in data science, powers everyday products such as movie selection, spam filters, and social media feeds. Machine learning involves automatically constructing models to explain and generalise datasets, integrating elements of statistics and algorithm development. Initially rooted in Artificial Intelligence, it encompasses various learning approaches. This unit provides you with the fundamental principles of machine learning, enabling you to apply supervised and unsupervised learning methods, explore basic deep learning principles, investigate generative AI and large language models (LLMs) and gain practical experience in solving industry-relevant data-driven problems. This introductory unit is suitable for students with diverse backgrounds in data science and other majors. It provides hands-on experience and empowers you with the skills and knowledge necessary to excel in an era driven by data and Artificial Intelligence.

Unit information

Unit code: IFQ580
Faculty: Faculty of Science
School/Discipline: School of Computer Science
Study area: Science
Credit points: 12

Dates and locations

Teaching period	Dates	Locations
Session 1, 2027	1 February 2027 - 9 April 2027	QUT Online (Online)
Session 3, 2027	19 July 2027 - 24 September 2027	QUT Online (Online)

See all available class timetables for this unit

Previous study requirements

Prerequisites: IFQ509 or IFN509 OR IFN581 or IFQ581 or (( IFQ555 and IFQ556) or (IFN555 and IFN556)) or admission to IQ12
Equivalents: IFN580

Guide to previous study requirements

Prerequisites: To enrol in this unit, you must have completed these prerequisite units (or have credit, advanced standing or exemption for them), or be able to demonstrate that you have equivalent background knowledge.
Anti-requisites: You can’t enrol in this unit if you have completed any of these anti-requisite units.
Co-requisites: To enrol in this unit, you must have already completed these co-requisite units, or you must enrol in them at the same time.
Equivalents: You can’t enrol in this unit if you have completed any of these equivalent units.
Assumed knowledge: We assume that you have a minimum level of knowledge in certain areas before you start this unit.

Unit outlines

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Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of the teaching period.