In Progress
Please note that this Unit Profile is still in progress. The content below is subject to change.Overview
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
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).
Offerings For Term 1 - 2025
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.
Class Timetable
Assessment Overview
Assessment Grading
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%). Consult the University's Grades and Results Policy for more details of interim results and final grades.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
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.
Feedback from Student Feedback
Some students find it difficult to understand Particle Swarm Optimisation (PSO) and genetic programming.
A use case with sample coding will be helpful.
Feedback from Unit Coordinator Reflection
Python is a more appropriate industry-standard programming language to prepare industry-ready graduates in AI.
Introduce Python and Cloud Technology to Solve AI Problems as per unit update plan.
- 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)
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Practical Assessment - 30% | ||||
| 2 - Written Assessment - 25% | ||||
| 3 - Project (applied) - 45% | ||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Knowledge | ||||
| 2 - Communication | ||||
| 3 - Cognitive, technical and creative skills | ||||
| 4 - Research | ||||
| 5 - Self-management | ||||
| 6 - Ethical and Professional Responsibility | ||||
| 7 - Leadership | ||||
| 8 - Aboriginal and Torres Strait Islander Cultures | ||||