Soft Computing PG (7197.6)
| 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 |
| School Of Information Technology & Systems | Post Graduate Level | Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021) |
This unit may be cotaught with 7168 Soft Computing.
Learning outcomes
On successful completion of this unit, students will be able to:1. Design and implement artificial neural networks for classification and regression tasks;
2. Synthesise and apply knowledge to solve problems involving uncertainty and imprecision using fuzzy logic;
3. Create genetic algorithms for solving optimization problems; and
4. Analyse and compare the performance of different soft computing techniques in realistic problems.
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 development3. 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
11512 Pattern Recognition and Machine Learning PG and 11521 Programming for Data Science G OR8995 Software Technology 1 G
Incompatible units
7168 Soft ComputingEquivalent units
None.Assumed knowledge
Basic mathematics.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|---|---|---|---|---|
| 2026 | Bruce, Canberra | Semester 2 | 10 August 2026 | On-campus | Dr Min Wang |
| 2027 | Bruce, Canberra | Semester 2 | 09 August 2027 | On-campus | Dr Min Wang |
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.
GenAI
GenAI - Guided: Students will be guided in how GenAI must/may be used in completing the assessment as detailed in the unit outline and assessment instructions. More detailed information can be found at GenAI and Assessment at UC. Guided: Means the convener will permit students to ethically use GenAI in some assessments but not all of them, with guidance and as outlined by each assessment description. The Library Guide provides further information, including how to
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 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 , , and ÎçÒ¹AV (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 quiz.
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
