Overview
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
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).
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 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.
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 Evaluations
The transition to AWS SageMaker posed challenges for students.
It is recommended to remove SageMaker to revert to Jupyter Notebook for lab activities.
Feedback from Student Evaluations
One of the assessment deadlines was adjusted during the term, which created some confusion among the students.
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.
- Select Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks.
The Australian Computer Society (ACS) recognises the Skills Framework for the Information Age (SFIA). SFIA provides a consistent definition of ICT skills. SFIA is adopted by organisations, governments, and individuals in many countries and is increasingly used when developing job descriptions and role profiles.
ACS members can use the tool MySFIA to build a skills profile at https://www.acs.org.au/professionalrecognition/mysfia-b2c.html.
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 7 (the SFIA code is included)
- Analytics (INAN)
- Systems design (DESN)
- Data modelling and design (DTAN)
- Programming/Software Development (PROG)
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| 1 - Online Quiz(zes) - 35% | |||
| 2 - Group Work - 30% | |||
| 3 - Written Assessment - 35% | |||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| 1 - Communication | |||
| 2 - Problem Solving | |||
| 3 - Critical Thinking | |||
| 4 - Information Literacy | |||
| 5 - Team Work | |||
| 6 - Information Technology Competence | |||
| 7 - Cross Cultural Competence | |||
| 8 - Ethical practice | |||
| 9 - Social Innovation | |||
| 10 - First Nations Knowledges | |||
| 11 - Aboriginal and Torres Strait Islander Cultures | |||
Alignment of Assessment Tasks to Graduate Attributes
| Assessment Tasks | Graduate Attributes | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
| 1 - Online Quiz(zes) - 35% | |||||||||||
| 2 - Group Work - 30% | |||||||||||
| 3 - Written Assessment - 35% | |||||||||||
Textbooks
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Third Edition (18 October 2022)
- Authors: Aurelien Geron
- O Reilly Media
- ISBN: 9781098125974
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Google Colab
- Jupyter Notebook 7 or latest
All submissions for this unit must use the referencing style: Harvard (author-date)
For further information, see the Assessment Tasks.
n.islam@cqu.edu.au
Week 1
Begin Date: 10 Mar 2025Module/Topic
Introduction to Artificial Intelligence, History and Applications
Chapter
Chapter 1
Events and Submissions/Topic
Tutorial: A review of Python for AI Applications
Week 2
Begin Date: 17 Mar 2025Module/Topic
Machine Learning, Model and Tools
Chapter
Chapter 1
Events and Submissions/Topic
Lab : AWS Cloud Academy -Machine Learning
Week 3
Begin Date: 24 Mar 2025Module/Topic
Predictive Models: How to Predict
Chapter
Events and Submissions/Topic
Regression-Hands-on Lab Activity
Week 4
Begin Date: 31 Mar 2025Module/Topic
Introduction to Neural Networks
Chapter
Events and Submissions/Topic
Perceptron-Hands-on Lab Activity
Week 5
Begin Date: 07 Apr 2025Module/Topic
Recognition with Computer Vision-CNN
Chapter
Events and Submissions/Topic
CNN-Hands-on Lab Activity
Knowledge Check quizzes, Due: Week 5 Friday (11 Apr 2025) 11:45 pm AEST
Vacation Week
Begin Date: 14 Apr 2025Module/Topic
Session Break
Chapter
Events and Submissions/Topic
Week 6
Begin Date: 21 Apr 2025Module/Topic
Object Detection and Tracking
Chapter
Events and Submissions/Topic
Lab Activity
Week 7
Begin Date: 28 Apr 2025Module/Topic
Natural Language Processing
Chapter
Events and Submissions/Topic
Lab Activity
Week 8
Begin Date: 05 May 2025Module/Topic
Generative AI (GANS)
Chapter
Events and Submissions/Topic
Lab Activity
AI Project Due: Week 8 Friday (9 May 2025) 11:45 pm AEST
Week 9
Begin Date: 12 May 2025Module/Topic
Ethical, Responsible and Explainable AI
Chapter
Events and Submissions/Topic
Tutorial Discussion
Week 10
Begin Date: 19 May 2025Module/Topic
Robotics and Q-learning
Chapter
Events and Submissions/Topic
Lab Activity
Week 11
Begin Date: 26 May 2025Module/Topic
AI on Cloud
Chapter
Events and Submissions/Topic
AWS Cloud Academy Machine Learning
Week 12
Begin Date: 02 Jun 2025Module/Topic
Revision
Chapter
Events and Submissions/Topic
Revisions:
AI Ethics Report Due: Week 12 Friday (6 June 2025) 11:45 pm AEST
Unit Coordinator: Dr. Nahina Islam
Email: n.islam@cqu.edu.au
1 Online Quiz(zes)
As part of this assessment, students will be invited to enroll in the AWS Academy Machine Learning Foundations course. They may complete any three quizzes, such as the Module 2 Knowledge Check. Each module includes a knowledge check quiz. The scores of the completed quizzes will contribute 35% to the total assessment for this subject.
1
Other
Week 5 Friday (11 Apr 2025) 11:45 pm AEST
Week 7 Friday (2 May 2025)
Scores will be made available.
The AWS Academy Machine Learning Foundations course consists of eight modules. The first four modules are foundational, and students need to complete quizzes for any three of these modules. All students will be invited to enroll and should be able to complete the course online. Each quiz contains 10-15 multiple-choice questions, with no time restrictions on each quiz. The scores of the completed quizzes will contribute 35% to the total assessment for this subject.
- Select Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges
- Problem Solving
- Critical Thinking
- Information Literacy
- Information Technology Competence
2 Group Work
Assignment 2 is a group project where students work together to write Python code for specific problem-solving tasks. They are required to choose and justify the use of AI tools & techniques for these tasks. Details, including the project description, data, and sources, are provided on the Moodle site. The unit coordinator assigns groups with a maximum size of three. Individual contributions are assessed, and while group members may receive similar marks based on participation, each student's unique contribution is considered.
This assessment will address the following unit learning outcomes: Apply industry tools to solve AI problems and critique business cases for AI systems against social and ethical frameworks.
Week 8 Friday (9 May 2025) 11:45 pm AEST
Week 10 Friday (23 May 2025)
A detailed rubric and marking criteria will be made available on Moodle as part of the comprehensive assessment description. This document will provide clear guidelines for the evaluation process, offering transparency on how assignments will be assessed and graded. Students are encouraged to refer to this resource for a thorough understanding of the expectations and criteria that will inform the assessment of their work
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks.
- Communication
- Problem Solving
- Critical Thinking
- Information Literacy
- Team Work
- Information Technology Competence
- Cross Cultural Competence
- Ethical practice
- Social Innovation
3 Written Assessment
Assignment 3 is an individual task where students develop a written artefact (document) on a business case specified by the unit coordinator. Students must apply the knowledge and skills acquired throughout the unit to prepare this document. This assessment will address the following unit learning outcomes: select Artificial Intelligence (AI) techniques to solve authentic problems, including social innovation challenges.
Week 12 Friday (6 June 2025) 11:45 pm AEST
Upon grade certification
Assignment 3 is an individual task where students develop a written artifact (document) on a business case specified by the unit coordinator. The work will be evaluated based on the quality of learning from the subject content and the knowledge of international AI standards and frameworks for ethical AI in the provided business scenario. A detailed rubric will be provided on Moodle.
- Select Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks.
- Communication
- Problem Solving
- Critical Thinking
- Information Literacy
- Information Technology Competence
- Cross Cultural Competence
- Ethical practice
- Social Innovation
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.
What can you do to act with integrity?
