COIT12213 - Applied Artificial Intelligence

General Information

Unit Synopsis

Artificial Intelligence (AI) involves developing systems that are autonomous and intelligent. This unit introduces you to contemporary and emerging AI technologies to address problems such as medical diagnosis, manufacturing optimisation and transport scheduling. You will investigate the application of AI technologies in areas such as computer vision, machine learning and deep learning. Fundamental AI concepts will be considered, including artificial neural networks and model validation techniques. You will develop AI systems using industry tools and learn to develop a business case for an AI system.

Details

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

Pre-requisite: COIT11222 Programming Fundamentals


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

Term 1 - 2026 Profile
Online
Term 2 - 2026 Profile
Brisbane
Cairns
Melbourne
Online
Rockhampton
Sydney

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

This information will not be available until 8 weeks before term.
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%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

To view Past Exams,
please login
Previous Feedback

Term 1 - 2025 : The overall satisfaction for students in the last offering of this course was 100.00% (`Agree` and `Strongly Agree` responses), based on a 25% 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 Evaluations
Feedback
The transition to AWS SageMaker posed challenges for students.
Recommendation
It is recommended to remove SageMaker to revert to Jupyter Notebook for lab activities.
Action Taken
SageMaker was removed, and Jupyter Notebook was reinstated for lab activities, which restored seamless functionality.
Source: Student Evaluations
Feedback
One of the assessment deadlines was adjusted during the term, which created some confusion among the students.
Recommendation
Any changes to assessments during the term will be clearly labelled with differences from the previous version and with a short video explaining the update.
Action Taken
No assessment deadlines were changed during this term. We adhered to the deadlines as specified in the unit profile.
Source: Unit coordinator's reflection
Feedback
The current design of Assignment 1 does not adequately reflect the applied and analytical nature of the Applied Artificial Intelligence.
Recommendation
At present, Assessment 1 is designed as an online quiz, which may not effectively evaluate students’ depth of understanding and conceptual application in Applied Artificial Intelligence. It is recommended that this task be redesigned as a practical, coding-based assessment to more accurately measure students’ applied knowledge, analytical thinking, and problem-solving capabilities.
Action Taken
In Progress
Source: Unit coordinator's reflection
Feedback
Assignments 2 and 3 could be strengthened in terms of assessment rigour. The current format may not fully capture students’ depth of understanding.
Recommendation
Incorporate a viva or presentation component into Assignments 2 and 3 to more effectively assess students’ conceptual understanding. This will allow markers to directly assess each student’s understanding, confirm authorship of their work, and enhance the overall rigour and integrity of the assessment process.
Action Taken
In Progress
Unit learning Outcomes
This information will not be available until 8 weeks before term.
To see Learning Outcomes from an earlier availability, please search via a previous term.