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

1. UC graduates are professional - communicate effectively
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
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - think globally about issues in their profession
2. UC graduates are global citizens - understand issues in their profession from the perspective of other cultures
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. UC graduates are lifelong learners - be self-aware
3. UC graduates are lifelong learners - evaluate and adopt new technology
3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development


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 2025 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
2024 Bruce, Canberra Semester 2 29 July 2024 On-Campus Dr Aya Hussein
2025 Bruce, Canberra Semester 2 28 July 2025 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 much of the course content is covered well in the following textbooks: Artificial Intelligence: An Introduction to Intelligent Systems. 2nd Edition, Michael Negnevitsky, Addison Wesley.

The definitive book on Artificial intelligence is "Artificial Intelligence: A Modern Approach, Global Edition (3e) By Stuart Russell, Peter Norvig" is also helpful.

Submission of assessment items

Extensions & Late submissions

  • All marked tutorials and assignments will be submitted on Canvas, some also involve a demonstration marked in tutorial class.
  • Quizzes will be done during lecture times. These Quizzes are available at that time only. 

Special assessment requirements

  • It is necessary to pass the Quizzes. If you fail to get 25 of the 50 Quiz marks you will fail the unit.
  • Assignment demonstrations (if required) are not optional. You must demonstrate assignments when required. It is your responsibility to arrange with the tutor or unit convenor when to demonstrate your assignments if you cannot attend on the prescribed day(s).

Supplementary assessment

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

Academic integrity

Students have a responsibility to uphold University standards on ethical scholarship. Good scholarship involves building on the work of others and use of others' work must be acknowledged with proper attribution made. Cheating, plagiarism, and falsification of data are dishonest practices that contravene academic values. Refer to the University's Student Charter for more information.

To enhance understanding of academic integrity, all students are expected to complete the Academic Integrity Module (AIM) at least once during their course of study. You can access this module within UCLearn (Canvas) through the 'Academic Integrity and Avoiding Plagiarism' link in the Study Help site.

Use of Text-Matching Software

The University of Canberra uses text-matching software to help students and staff reduce plagiarism and improve understanding of academic integrity. The software matches submitted text in student assignments against material from various sources: the internet, published books and journals, and previously submitted student texts.

Important Note on the combining of Assignment and Exam marks

 

  • NOTE 1: It is necessary to pass the Quiz part of the unit. If you fail the Quizzes your final grade will be NX Fail regardless of your total unit mark.
  • NOTE 2: The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes, which may result in an adjustment to the marks awarded for a specific task.
  • NOTE 3: The lecturer/moderator reserves the right to increase a student’s mark for academic merit. This is done rarely but can be done for: consistency, elegance, forum or class participation or creativity

Learner engagement

Lectures: 24 hours

Tutorials: 22 hours

Marked tutorial 1: 4 hours

Marked tutorial 2: 4 hours

Assignment 1: 20 hours

Assignment 2: 20 hours

Quizz 1: 20

Quizz 2: 36

Total 150 hours 

Participation requirements

Your participation may involve demonstrations for any submitted assessments. Your attendance is required to do the Quizzes. No other participation is mandatory.

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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