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 3 - 2024

Term 1 - 2025 Profile
Online
Term 2 - 2025 Profile
Online

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. Online Quiz(zes) 35%
2. Group Work 30%
3. Written Assessment 35%

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

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Previous Feedback

Term 1 - 2023 : The overall satisfaction for students in the last offering of this course was 80.00% (`Agree` and `Strongly Agree` responses), based on a 31.25% 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: Teaching Team
Feedback
Students feel overloaded with many new theoretical and practical concepts each week, making it difficult for some students to grasp key AI concepts.
Recommendation
Increase practical materials on important AI topics, such as image analysis, face recognition and deep learning models, while reducing some of the theory on less important topics.
Action Taken
In Progress
Source: Head of Postgraduate ICT courses
Feedback
The Moodle site can be streamlined to make it more user-friendly and consistent to adhere with CQURenew guidelines.
Recommendation
Streamline the Moodle site to make it more consistent to adhere with CQURenew guidelines.
Action Taken
In Progress
Unit learning Outcomes

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

  1. Select Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges
  2. Apply industry tools to solve AI problems
  3. 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) •
2 - Group Work • •
3 - Written Assessment • • •
Alignment of Graduate Attributes to Learning Outcomes
• Introductory Level
• Intermediate Level
• Graduate Level
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 •
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
2 - Group Work • • • • • • • • •
3 - Written Assessment • • • • • • • •
1 - Online Quiz(zes) • • • •