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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).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 2 - 2024

Term 2 - 2024 Profile
Melbourne
Online
Term 1 - 2025 Profile
Brisbane
Melbourne
Online
Sydney
Term 2 - 2025 Profile
Melbourne
Online
Sydney
Term 1 - 2026 Profile
Brisbane
Melbourne
Online
Sydney
Term 2 - 2026 Profile
Brisbane
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

Assessment Task Weighting
1. Written Assessment 30%
2. Written Assessment 25%
3. Written Assessment 45%

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 - 2024 : The overall satisfaction for students in the last offering of this course was 70.00% (`Agree` and `Strongly Agree` responses), based on a 33.33% 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
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
The topics on Particle Swarm Optimisation (PSO) and Genetic Programming were grouped under a weekly lecture on metaheuristic algorithms with coding examples and use cases in Term 1 2024.
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 the most recent offering of the unit, that is 2024 HT1, both Python and Cloud AI have been discussed and applied in assessments.
Source: Student Unit and Teaching Evaluations
Feedback
Students found it challenging to transition to Python coding, particularly as many of their previous programming experiences were primarily in Java. This made it difficult to engage with the programming language specific content early in the term.
Recommendation
COIT20245 (Introduction to Programming), a core PG unit since Term 1 2024, now teaches Python, providing the necessary background for COIT20277. For students without this background, key Python concepts can be briefly reviewed in the first two weeks of term.
Action Taken
In Progress
Source: Student Unit and Teaching Evaluations
Feedback
Students expressed a desire for more detailed explanations and practical examples to better understand abstract or complex concepts covered in lectures.
Recommendation
Incorporate additional real-world examples and case studies into weekly lectures and tutorials to enhance conceptual understanding and application. These examples will be used to demonstrate key ideas and support learning outcomes.
Action Taken
In Progress
Source: Student Unit and Teaching Evaluations
Feedback
Some students noted that feedback on assessments could be more actionable and consistent in terms of clarity and usefulness.
Recommendation
Encourage a coordinated approach among teaching staff to ensure feedback is clear, specific, and consistently aligned with assessment criteria. The teaching team will implement a moderation process for assessment feedback to ensure it is constructive, consistent, and valuable for student learning and improvement.
Action Taken
In Progress
Unit learning Outcomes

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

  1. Model internal representation, performance criteria, and computational components identifying elements of authentic problems to apply neural, fuzzy or evolutionary computation
  2. Create effective and efficient computational intelligence solutions to authentic problems
  3. Evaluate the solution to a computational intelligence problem, analysing the merits and demerits of the chosen approach
  4. Investigate the potential to enhance the model using one or more computational intelligence techniques.

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.

This unit contributes to the following workplace skills as defined by SFIA. The SFIA code is included:

  • Data modelling and design (DTAN)
  • Software design (SWDN)
  • Programming/Software Development (PROG)
  • Testing (TEST)
  • Application Support (ASUP)

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Written Assessment • •
2 - Written Assessment • • •
3 - Written Assessment • • •
Alignment of Graduate Attributes to Learning Outcomes
• Professional Level
• Advanced Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Knowledge • • • •
2 - Communication • • • •
3 - Cognitive, technical and creative skills • • • •
4 - Research •
5 - Self-management • • • •
Alignment of Assessment Tasks to Graduate Attributes
• Professional Level
• Advanced Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8
1 - Written Assessment • • • •
2 - Written Assessment • • • •
3 - Written Assessment • • • • •