Introduction to Artificial Intelligence unit (KIT509)

Melbourne Study Centre, Ultimo Study Centre, Hobart

Introduction

This unit provides an introduction to the core concepts, methods, and applications of artificial intelligence. Students explore major AI approaches, including symbolic methods, supervised learning, unsupervised learning, reinforcement learning, generative models, and large language models. The unit focuses on developing a clear understanding of how these systems work, where they are effective, and what limitations, assumptions, and trade-offs underlie their behaviour.

Across weekly hands-on activities, students learn to apply and evaluate simple machine learning models, interpret a range of AI outputs, compare the strengths of different techniques, and understand how evaluation methods guide model selection in different contexts. Practical work emphasises the use of appropriate data-handling and evaluation methods, as well as the importance of robustness, context, and ethical considerations when developing or analysing AI systems.

By the end of the unit, students will be able to interpret and critically assess AI systems and outputs, understand their societal and ethical implications, and form a reasoned perspective on contemporary and emerging AI technologies. They will also develop practical skills in working with data, training and evaluating models, and communicating AI results clearly and appropriately.

Summary

Unit name	Introduction to Artificial Intelligence
Unit code	KIT509
Credit points	12.5
College/School	Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Doctor Robert Ollington
Delivered By	University of Tasmania and Third Party(ies): ECA
Level	Postgraduate

Availability

Location	Study period	Attendance options		Available to
Melbourne Study Centre	Semester 1
Ultimo Study Centre	Semester 1
Hobart	Semester 1
Hobart	Semester 2

Note

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Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 1	22/02/2027	16/03/2027	19/04/2027	13/06/2027
Semester 2	05/07/2027	27/07/2027	30/08/2027	24/10/2027

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2027 are indicative and subject to change. Finalised census dates for 2027 will be available from the 1st October 2026. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

• Compare and critically evaluate major approaches in artificial intelligence in terms of their core principles, typical applications, strengths, limitations, and suitability for different problem contexts.
• Select and apply appropriate introductory AI and machine learning techniques to explore data or agent behaviour, and interpret and justify results in context.
• Evaluate AI models and outputs using appropriate evaluation methods, critiquing robustness, assumptions, data quality, and limits to validity.
• Critically analyse societal, ethical, and practical implications of contemporary AI systems, drawing justified conclusions for professional practice.

Fee Information

Field of Education	Commencing Student Contribution 1,3	Grandfathered Student Contribution 1,3	Approved Pathway Course Student Contribution 2,3	Domestic Full Fee 4
020119	$1,235.00	$1,235.00	not applicable

• Available as a Commonwealth Supported Place
• HECS-HELP is available on this unit, depending on your eligibility³
• FEE-HELP is available on this unit, depending on your eligibility⁴

¹ Please refer to more information on student contribution amounts.
² Please refer to more information on eligibility and Approved Pathway courses.
³ Please refer to more information on eligibility for HECS-HELP.
⁴ Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Teaching

Teaching Pattern	On-Campus Melbourne and Sydney teaching arrangements may differ and will be advised by your teaching team. On-Line teaching arrangements may differ and will be advised by your teaching team. For on-campus Hobart delivery: Independent Learning: 2 hours/week Online Workshops: 2 hours/week Lab Classes (Tutorials): 2 hours/week
Assessment	Tutorial work (30%)|Assignment 1 (35%)|Assignment 2 (35%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.