FIT1063 · Topics in computing

Overview

This unit introduces you to the breadth of modern computing through a rotating sequence of research-led topics presented by Faculty academics. It showcases how computer scientists formulate questions, model problems, design algorithms, build systems, analyse data, and evaluate evidence across domains such as discrete optimisation, knowledge representation and automated reasoning, planning, machine learning and deep learning, computer vision, neuro-symbolic AI, data science, computational statistics, cybersecurity, theoretical computer science, programming languages, distributed systems, software engineering, human-computer interaction, robotics, data visualisation, and other emerging areas of modern computing and information technology. You will learn how different research areas define problems, choose methods, assess quality, and create scientific, industrial, and societal impact. Through guided reflection, discussion, and short exploratory tasks, the unit helps you identify areas of interest, understand pathways through the degree, and prepare for later engagement in projects, honours, internships, or research with Faculty academics.

Offerings

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Describe the core questions, computational methods, and application contexts of a range of computer science and information technology research areas, and explain how theoretical, data-driven, systems-oriented, and human-centred approaches differ;
2. Compare selected computing domains by analysing the kinds of problems they address, the models, data, and tools they use, and the criteria by which their solutions are evaluated;
3. Locate, interpret, and communicate key ideas from research talks, demonstrations, and introductory academic or technical sources using appropriate written, verbal, diagrammatic, or visual forms;
4. Discuss the scientific, industrial, and societal significance of selected computing research areas, including ethical, privacy, security, accessibility, environmental, and inclusion considerations relevant to responsible innovation;
5. Reflect on personal interests, strengths, and learning goals to identify future study, project, and research pathways, using AI-enabled and other exploratory tools responsibly, critically, and transparently.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.