Soft Computing PG (7197.5)

Please note these are the 2025 details for this unit
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Soft Computing PG
Available teaching periods Delivery mode Location
View teaching periods On-campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
Academic Program Area - Technology Post Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
This is an advanced unit in artificial intelligence emphasizing the Soft Computing discipline. Each semester, coverage is of two or three topics from Artificial Neural Networks, Fuzzy Systems, Genetic Algorithms, Software Agents, Artificial Life, and Complex Adaptive Systems. The unit content covers Soft Computing theory, fusion of two or more Soft Computing techniques and their applicability to real world applications. The choices depend on the availability of lecturers, student interest and research activity in the School. The unit has a strong practical focus with the implementation of Soft Computing techniques in laboratory exercises and assignments.
This unit may be cotaught with 7168 Soft Computing.

Learning outcomes

On successful completion of this unit, students will be able to:
1. Describe, argue for and critique the Soft Computing discipline. Students will be able to use at least two of the Soft Computing techniques; and
2. Given an artificial intelligence project, a student will be able to: identify and select a suitable Soft Computing technology to solve the problem; construct a solution and implement a Soft Computing solution.


Graduate attributes

3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development
3. UC graduates are lifelong learners - evaluate and adopt new technology
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas


Prerequisites

None.

Corequisites

Prior or concurrent completion of 11512 Pattern Recognition and Machine Learning PG AND 11521 Programming for Data Science G or 8995 Software Technology 1 G OR
Enrolment in 354JA Master of Engineering or 846AA Master of Information Technology.

Incompatible units

7168 Soft Computing

Equivalent units

None.

Assumed knowledge

Basic mathematics.
Availability for enrolment in 2026 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2025 Bruce, Canberra Semester 2 28 July 2025 On-campus Dr Aya Hussein
2026 Bruce, Canberra Semester 2 10 August 2026 On-campus Dr Aya Hussein
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

Required texts

There is no mandatory textbook for this unit.

However, the following textbooks can be useful:

1. For Artificial Neural Networks: "Neural Networks and Deep Learning" by Michael Nielsen which is available online for free at http://neuralnetworksanddeeplearning.com/

2. For Fuzzy Logic: "An Introduction to Fuzzy Logic for Practical Applications" by Kazuo Tanaka

3. For Genetic Algorithms: "An Introduction to Genetic Algorithms" by Melanie Mitchell

Submission of assessment items

Extensions & Late submissions

  • All marked tutorials and assignments will be submitted on Canvas and orally discussed as per the oral discussion schedule.
  • Quiz will be conducted during lecture times. The quiz is available at that time only. 

Special assessment requirements

  • ¿¿The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.
  • All assignments & marked in-class activities have oral discussions
  • Oral discussions are not optional. It is your responsibility to arrange with the tutor or unit convenor when to orally disucuss your assignments if you cannot attend on the prescribed day(s).
  • Failure to attend the oral discussion will result in a score of zero for the corresponding assessment.

Supplementary assessment

Supplementary assessment is not offered in this unit unless required by the relevant university policy.

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Lectures: 24 hours

Tutorials: 22 hours

Marked tutorial 1: 2 hours

Assignment 1 - Programming assignment for prediction and control tasks part 1: 12 hours

Assignment 2 - Programming assignment for prediction and control tasks part 2: 13 hours

Assignment 3 - Pogramming assignment to solve realistic optimisation problems: 10 hours

Quizz: 13 hours

Reading: 40 hours

Research: 14 hours

Total 150 hours 

Participation requirements

Your participation involves oral discussion for all assignments and in-cleass activities. 

Your attendance is required to do the quizz. 

Required IT skills

Students are required to have programming experience with a high-level programming language (e.g., C++, Java, C#, Matlab or Python) prior to staring the unit.


 

In-unit costs

None

Work placement, internships or practicums

Not Applicable

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