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 | KIT108 |
| Credit points | 12.5 |
| College/School | Sciences and Engineering School of Information and Communication Technology |
| Discipline | Information & Communication Technology |
| Coordinator | Doctor Robert Ollington |
| Available as an elective? | Yes |
| Delivered By | University of Tasmania and Third Party(ies): ECA |
| Level | Introductory |
Availability
Specific information on 2027 unit availability will be available in August
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
2027 fee information will be available in August.
Teaching
| Teaching Pattern | On-Campus Melbourne and Sydney teaching arrangements may differ and will be advised by your teaching team. On-Campus enrolments in Hobart and Launceston:
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|---|---|
| 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 |
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.