COIT13242 - Artificial Intelligence for Vision Analytics

Showing: 2026 HE Term 1
General Information

Unit Synopsis

In this unit, you will explore state-of-the-art Artificial Intelligence (AI) techniques for vision and image processing, with an emphasis on real-world applications. You will build a solid foundation in machine learning and deep learning concepts, including regression, regularisation, optimisation, convolutional neural networks (CNNs), supervised learning models, and attention-based transformer architectures for vision tasks. You will apply these techniques to different datasets such as hyperspectral imaging, high-resolution RGB imagery, and dynamic video sequences captured from drones, robotic systems, and smart sensors. Through hands-on programming and case-based learning, you will learn how to formulate practical vision problems, such as detection, segmentation, tracking, and scene understanding, as AI tasks and develop robust solutions using state-of-the-art deep learning frameworks. This unit follows an end-to-end workflow approach. You will gain experience in data preprocessing and annotation, model design and training, performance evaluation using quantitative and visual metrics, and deployment of AI models for real-time and embedded vision systems. By the end of the unit, you will be able to design, implement, and deploy deep learning pipelines for image and video analysis, equipping you with the skills needed to contribute to modern machine vision, robotics, and intelligent decision-support applications.

Details

Level Undergraduate
Unit Level 3
Credit Points 6
Student Contribution Band SCA Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Pre-requisites: COIT12213 - Introduction to Artificial Intelligence and COIT12209 - Data Science.

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).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Undergraduate 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 25%
2. Practical Assessment 35%
3. Written Assessment 40%

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

Past Exams

Previous Feedback

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.

Unit Learning Outcomes

On successful completion of this unit, you will be able to:

  1. Explain fundamental concepts in computer vision and deep learning, including key tasks such as image classification, localisation, and object detection
  2. Design, implement, and train neural network models for visual recognition tasks using modern deep learning frameworks
  3. Evaluate and improve model performance using practical techniques such as fine-tuning of neural networks
  4. Apply deep learning methods to real-world problems and to develop end-to-end solutions for computer vision tasks.

The Australian Computer Society (ACS), the professional association for Australia's ICT sector, 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):

  • Machine learning (MLNG) 
  • Artificial intelligence (AI) and data ethics (AIDE)
  • Software design (SWDN)
  • Programming/software development (PROG)
  • Data visualisation (VISL)
  • Functional testing (TEST)
  • Application Support (ASUP)
Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Practical Assessment • • •
2 - Practical Assessment • • •
3 - Written Assessment • •
Alignment of Graduate Attributes to Learning Outcomes
• Introductory Level
• Intermediate Level
• Graduate Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Communication • •
2 - Problem Solving • • • •
3 - Critical Thinking •
4 - Information Literacy • • •
6 - Information Technology Competence •
8 - Ethical practice •
Alignment of Assessment Tasks to Graduate Attributes
• Introductory Level
• Intermediate Level
• Graduate Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8 9 10 10