Unit Synopsis
This unit introduces deep learning for applied computer vision, focusing on perception and decision-making problems in autonomous systems. You will build a solid foundation in supervised learning, neural networks, convolutional architectures, and state-of-the-art vision models. You will apply these techniques to real-world datasets, including RGB images, hyperspectral data, drone imagery, and sports video, gaining hands-on experience with data from robotic and sensor platforms. Through case-based learning and programming exercises, you will formulate practical problems as learning tasks, design and train models, and evaluate their performance using quantitative metrics and visual analysis. The unit takes an end-to-end perspective, covering data preprocessing, data annotation, model training, optimisation, testing, evaluation, and deployment. By the end of the unit, you will be able to build deployable deep learning pipelines suitable for use in robotic platforms and decision-support systems across various domains.
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: COIT20277 Introduction to Artificial Intelligence.
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 - 2027
Term 1 - 2027 Profile
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
To see assessment details from an earlier availability, please search via a previous term.
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.
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: Unit Coordinator Reflection
Students would benefit from strengthening their understanding of mathematical foundations, particularly linear algebra and probability.
Introduce additional scaffolding materials or pre-assessment refreshers on linear algebra and probability to better prepare students for advanced topics such as CMA-ES and other metaheuristic algorithms.
This was not an issue because the unit name and content had been changed.
Source: Unit Coordinator Reflection
Students would benefit from step-by-step examples and visual demonstrations to better connect algorithm theory with implementation.
Incorporate clear, step-by-step working examples and visual demonstrations to help students connect algorithm theory with implementation.
In the new teaching content, step-by-step examples are widely used in the tutorial activities.
Source: UC reflection
Students can use AI to achieve very good assessment results, making it difficult for the assessment to accurately and fairly reflect their individual performance.
Improve assessment design to reduce inappropriate AI use, for example, by incorporating open-ended, competition-based tasks with no standard answers that require students to demonstrate originality and critical thinking.
In Progress
Source: UC reflection
The current marking criteria are too broad to accurately assess the quality of student work.
Develop more detailed marking criteria that clearly align assessment tasks with the intended learning outcomes.
In Progress
To see Learning Outcomes from an earlier availability, please search via a previous term.