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Unit of Study COMP2009 - Fundamentals of Artificial Intelligence and Machine Learning (2027)

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Unit Snapshot

  • Enrolment information

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Unit description

This unit focuses on essential AI principles and concepts, machine learning models, AI security and privacy. It covers text, image, vision, and generative AI.

Unit content

Module 1:  Introduction to Artificial Intelligence Concepts

Module 2:  Fundamentals of Computer Vision

Module 3:  Fundamentals of Text Analysis with the Language Service

Module 4:  Fundamentals of Document Intelligence and Knowledge Mining

Module 5:  Fundamentals of Generative Artificial Intelligence

Module 6:  Artificial Intelligence Trends and Future Perspectives

Availabilities

Location Domestic International
Brisbane
Term2
Term2
China - Guangxi UST
Term2
Term2
Gold Coast
Term2
Term2
Melbourne
Term2
Term2
Online
Term2
Term2
Papua New Guinea - IBSU Port Moresby
Term2
Term2
Sydney
Term2
Term2

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

apply fundamental principles of machine learning including security and privacy considerations to relevant datasets

apply machine learning solutions to real-world problems.

design and implement machine learning solutions to address complex, real-world problems using industry-standard tools and techniques

assess various machine learning based solutions for predictive and analytical tasks using appropriate evaluation metrics.

Teaching and assessment

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Teaching method
Tutorial 2 hours (weekly)
Workshop 1 hour (weekly)
Assessment
Practical 30%
Practical 30%
Exam: Proctored, Online 40%

Prescribed learning resources

Prescribed Texts
  • Prescribed text information is not currently available.
Prescribed Resources/Equipment
  • Prescribed resources/equipment information is not currently available.

Prescribed Learning Resources may change in future Teaching Periods.