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PRT565 - Machine Learning, Artificial Intelligence and Algorithms

2025

Sector

Higher Education

Discipline

Information Technology

Credit Points

Equivalent Units

Prerequisites

Incompatible

Assumed Knowledge

HIT137 or PRT564 or SMA212 or SMA209 or equivalent.

Unit Description

This unit is aimed at developing a practical understanding of Machine Learning (ML) and Artificial Intelligence (AI) techniques. Students will have a good understanding of key algorithms used in ML and AI. They will learn when and how to use supervised and unsupervised learning techniques, build and validate models and analyse their performance. The learning activities will be centered around real-world problems chosen from different fields such as engineering, social and environmental applications. Students will use the Python programming language and relevant Python modules for their work in this unit.

Unit Learning Outcomes

1.	Critically evaluate and apply established machine learning and AI workflow and tools to synthesize complex information and develop predictive models.
2.	Choose appropriate supervised or non-supervised learning techniques for complex real-world applications through critical analysis and application of established machine learning theories.
3.	Apply and validate machine learning tools for developing innovative solutions and synthesizing complex information for solving real-world problems.
4.	Implement, validate and critically analyse machine learning algorithms.
5.	Critically analyse and identify possible ethical impacts of using Machine Learning (ML) and Artificial Intelligence (AI) tools.
6.	Interpret and communicate technical information effectively to technical and non-technical users especially at management level.
7.	Communicate technical information, related to ML and AI, effectively to non-technical users especially at management level

Assessment Overview

	Assessment Task	Value (of total mark)	Related Learning Outcome/s
1.	Online Quiz (12 Minutes)	5%	2,5
2.	Machine learning coding exercise (800 - 1000 words report explaining the implementation and validating code using appropriate test cases. The length of code will be around 800 - 1000 lines)	25%	1,2,3,4,6
3.	Artificial Intelligence Group Project 1 (6-8 minute presentation each)	30%	1,2,3,4,5,6,7
4.	Artificial Intelligence Group Project 2 (1000 - 1250 words each)	40%	1,2,3,4,5,6,7

Unit Availability

Year	Semester	Att Mode	Location	Learning Method
2025	Semester 2	Internal	CDU Sydney	OLR
2025	Semester 2	Internal	Danala - Education and Community Precinct	OLR
2025	Semester 2	External	External Studies	OLR

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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:
Fees and Payments
Student Services and Amenities Fees
2025 Domestic Unit Fees (PDF)