Unit Synopsis
Artificial Intelligence (AI) is transforming the way we interact with technology, enabling machines to think, learn, and adapt in ways that mimic human intelligence. From intelligent chatbots to autonomous robotics, AI is becoming an essential part of our everyday lives and has the potential to transform entire industries. This unit introduces the core concepts of AI, starting with foundational principles and real-world applications. You will explore key machine learning approaches, including both supervised and unsupervised learning, and examine advanced topics such as reinforcement learning, classical and heuristic search strategies, and deep learning, with a focus on convolutional and recurrent neural networks for tasks like image classification and natural language processing. Additionally, you will examine ethical AI practices, addressing the societal impact of AI and the importance of ensuring fairness, transparency, and accountability in AI systems. The unit also covers cutting-edge trends like cloud-based AI and AI at the edge, which are shaping the future of AI deployment. Through programming and problem-based assessments, you will gain both theoretical knowledge and practical skills in modern AI technologies.
Details
| Level | Postgraduate |
|---|---|
| Unit Level | 9 |
| Credit Points | 6 |
| Student Contribution Band | SCA Band 2 |
| Fraction of Full-Time Student Load | 0.125 |
| Pre-requisites or Co-requisites |
Pre-requisite: COIT20245 Introduction to Programming
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework). |
| Class Timetable | View Unit Timetable |
| Residential School | No Residential School |
Unit Availabilities from Term 1 - 2026
Attendance Requirements
All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Assessment Tasks
| Assessment Task | Weighting |
|---|---|
| 1. Practical Assessment | 30% |
| 2. Written Assessment | 25% |
| 3. Project (applied) | 45% |
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).
Past Exams
All University policies are available on the Policy web site, however you may wish to directly view the following policies below.
This list is not an exhaustive list of all University policies. The full list of policies are available on the Policy web site .
Term 1 - 2025 : The overall satisfaction for students in the last offering of this course was 75.00% (`Agree` and `Strongly Agree` responses), based on a 20% response rate.
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.
Source: Student Unit and Teaching Evaluations
Students found it challenging to transition to Python coding, particularly as many of their previous programming experiences were primarily in Java. This made it difficult to engage with the programming language specific content early in the term.
COIT20245 (Introduction to Programming), a core PG unit since Term 1 2024, now teaches Python, providing the necessary background for COIT20277. For students without this background, key Python concepts can be briefly reviewed in the first two weeks of term.
Following the recommendation, from 2025 T1 the unit offering was updated to include a review and practice exercises on Python coding during the first two weeks of the term.
Source: Student Unit and Teaching Evaluations
Students expressed a desire for more detailed explanations and practical examples to better understand abstract or complex concepts covered in lectures.
Incorporate additional real-world examples and case studies into weekly lectures and tutorials to enhance conceptual understanding and application. These examples will be used to demonstrate key ideas and support learning outcomes.
Implemented in 2024 T2 and 2025 T1 by adding real-world examples to lectures and tutorials, with assessments and the major project based on real-world datasets.
Source: Student Unit and Teaching Evaluations
Some students noted that feedback on assessments could be more actionable and consistent in terms of clarity and usefulness.
Encourage a coordinated approach among teaching staff to ensure feedback is clear, specific, and consistently aligned with assessment criteria. The teaching team will implement a moderation process for assessment feedback to ensure it is constructive, consistent, and valuable for student learning and improvement.
From 2025 T1, moderation of marked assessments was implemented. Marking has been largely aligned with the assessment criteria and consistent among tutors.
Source: Student feedback
Too much material and too little time.
This unit is designed as an introduction to AI, providing a broad overview of major subareas to prepare students for advanced units such as COIT29224 Evolutionary Computation and COIT29225 Neural Networks and Deep Learning. In response to feedback, the two weekly topics on Cloud AI and Edge AI will be combined into one week, allowing for extended coverage of Deep Learning with an additional week.
In Progress
Source: Student feedback
Students reported improved understanding of AI, with weekly quizzes suggested as a useful addition.
A summative quiz could be introduced in tutorials following major topic areas, such as Machine Learning or Search Techniques (typically spanning two to three weeks), to reinforce key concepts, provide timely feedback, and support ongoing student engagement.
In Progress
On successful completion of this unit, you will be able to:
- Explain key AI principles, including machine learning, deep learning, classical and heuristic search, and differentiate between supervised, unsupervised, and reinforcement learning paradigms.
- Implement machine learning models to solve real-world problems such as image classification and natural language processing in a modern programming language.
- Evaluate the role of emerging technologies, such as cloud-based AI and AI at the edge, in improving the efficiency and performance of AI applications.
- Examine responsible AI practices and ethical challenges in AI development, focusing on ethical standards and societal impact.
The Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA is adopted by organisations, governments and individuals in many countries and provides a widely used and consistent definition of ICT skills. SFIA is increasingly being used when developing job descriptions and role profiles. ACS members can use the tool MySFIA to build a skills profile.
This unit contributes to the following workplace skills as defined by SFIA 9 (the SFIA code is included):
- Data analytics (DAAN)
- Data science (DATS)
- Data engineering (DENG)
- Machine Learning (MLNG)
- Programming/Software Development (PROG)
| Assessment Tasks | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Practical Assessment | • | • | ||
| 2 - Written Assessment | • | • | ||
| 3 - Project (applied) | • | • | • | • |
| Graduate Attributes | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Knowledge | • | • | • | • |
| 2 - Communication | • | • | • | • |
| 3 - Cognitive, technical and creative skills | • | • | • | • |
| 5 - Self-management | • | • | • | • |
| Assessment Tasks | Graduate Attributes | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 8 | |