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HIT391 - Machine Learning: Advancements and Applications

2025

Sector

Higher Education

Discipline

Information Technology

Credit Points

Equivalent Units

Prerequisites

Incompatible

Coerequisites

Assumed Knowledge

Unit Description

In this unit students will develop a theoretical and practical understanding of different Machine Learning (ML) models used in real-world applications. Students will go through the key algorithms used for developing intelligent models. The focus will be on supervised methodologies model validation and performance analysis will also be explored. The learning activities will be centered on real-world problems chosen from different fields. Students will be exposed to coding for their work in this unit.

Unit Learning Outcomes

1.	Understand the general concepts and approaches used in building Machine Learning (ML) based models.
2.	Critically evaluate and apply established ML based algorithms to formulate predictive models.
3.	Develop an ability to critically analyse the problem domain to justify the type of ML Algorithm that needs to be used.
4.	Develop sound theoretical and practical competencies in Data Analysis and Preprocessing.
5.	Analyse and identify possible ethical impacts of using Machine Learning (ML) applications.

Assessment Overview

	Assessment Task	Value (of total mark)	Related Learning Outcome/s
1.	Ethics in using machine learning model (700-900 words report)	10%	1,5
2.	Machine learning coding exercise (500 word report + ~400 lines of code)	20%	1,2,3,5
3.	Machine learning Group Project ( 3-5 minutes presentation and 500 words report per student)	40%	1,2,3,4,5
4.	Online Test ( 2 hours)	30%	1,2,3,4

Unit Availability

Year	Semester	Att Mode	Location	Learning Method	Timetable*
2025	Semester 1	Internal	CDU Sydney	OLS	View
2025	Semester 1	Internal	Danala - Education and Community Precinct	OLR	View
2025	Semester 1	External	External Studies	OLR	View

* Timetable will be displayed in new tab or window depending on your browser's configuration; ensure Pop-ups are allowed for this site to view the timetable.

OL/OLS: Supplementary material will be provided on-line through Learnline. For more information about Learnline, please click here.

OLR: Internet access is required for this unit as students are required to access study materials provided on-line through Learnline. Please log into Learnline on the first day of each teaching period. For more information about Learnline, please click here.

Critical Dates

Important Dates for Students

Fee Information:
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