Artificial Intelligence unit (KXO108)

Shanghai Ocean University

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 and assumptions underlie their behaviour.

Across weekly hands-on activities, students learn to build 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. 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 assess AI, understand their societal and ethical implications, and form a critical perspective on contemporary and emerging AI technologies. They will also develop foundational practical skills in working with data, training models, and communicating AI results.

Summary

Unit name	Artificial Intelligence
Unit code	KXO108
Credit points	12.5
College/School	Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Miss Chunping Li
Available as an elective?	No
Delivered By	University of Tasmania and Third Party(ies): Shanghai Ocean University AEIN Institute
Level	Introductory

Availability

Location	Study period	Attendance options		Available to
Shanghai Ocean University	Shanghai Semester 1

Note

Enrolment in units available at ECA Melbourne, Hong Kong Universal Ed, and Shanghai Ocean University is only available to eligible students studying at those corresponding locations.

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Key Dates

Study Period	Start date	Census date	WW date	End date
Shanghai Semester 1	11/10/2027	16/11/2027	24/11/2027	07/01/2028

* 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

• Describe major approaches in artificial intelligence and explain their core principles, typical applications, and strengths and limitations.
• Apply basic AI and machine learning techniques to explore data or agent behaviour and interpret results in context.
• Evaluate AI models and outputs using appropriate evaluation methods, considering robustness, assumptions, and data quality.
• Critically analyse societal, ethical, and practical implications of contemporary AI systems.

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

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	Independent Learning: 2 hours/week Online Workshops: 2 hours/week Lab Classes (Tutorials): 2 hours/week
Assessment	Assignment 1 (30%)|Assignment 2 (30%)|Tutorial work (40%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	N/A
Recommended	N/A

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