COIT20277 - Introduction to Artificial Intelligence

General Information

Unit Synopsis

Artificial intelligence is closely related to the field called soft computing which provides a foundation for the conception, design, and deployment of intelligent systems directed towards intelligence and autonomy. This unit introduces you to the fundamental concepts of artificial intelligence in the three prominent areas of fuzzy systems, artificial neural networks, and evolutionary computation. You will be introduced to topics of genetic algorithms, evolutionary programming, and genetic programming. You will also be introduced to the most commonly used neural network paradigms. You will learn the concepts of fuzzy sets and fuzzy logic, and approximate reasoning, as part of fuzzy systems. The theoretical concepts will be reinforced with hands-on experience during computer lab tutorials.

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: COIT20245 Introduction to Programming


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).

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Unit Availabilities from Term 2 - 2025

Term 2 - 2025 Profile
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Melbourne
Online
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 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

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

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Previous Feedback

Term 1 - 2023 : The overall satisfaction for students in the last offering of this course was 75.00% (`Agree` and `Strongly Agree` responses), based on a 42.11% 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 feedback
Feedback
Link contents to real-world applications.
Recommendation
Initiate a content update to include materials that will cover real-world case studies and examples of artificial intelligence.
Action Taken
The assessment item covers real-world problems and solves them using a number of artificial intelligence techniques. All the problems relate to industry and looking for solutions, so students get a good understanding of how the learning will be used in the industry.
Source: Analysis by Unit Coordinator
Feedback
Need to focus more on applications of AI rather than theory. Based on the current industry trend consider using Python programming language instead of JAVA.
Recommendation
A unit update will be initiated to cover the basics of AI in the first 2/3 lectures then focus on the AI applications for data analysis, like healthcare, cybersecurity, etc, using Python based coding.
Action Taken
To address the feedback, we have laid out a plan that will be carried out in the coming term.
Source: Student Feedback
Feedback
Some students find it difficult to understand Particle Swarm Optimisation (PSO) and genetic programming.
Recommendation
A use case with sample coding will be helpful.
Action Taken
In Progress
Source: Unit Coordinator Reflection
Feedback
Python is a more appropriate industry-standard programming language to prepare industry-ready graduates in AI.
Recommendation
Introduce Python and Cloud Technology to Solve AI Problems as per unit update plan.
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.