Overview
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
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).
Offerings For Term 1 - 2022
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).
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.
Class Timetable
Assessment Overview
Assessment Grading
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.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
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.
Feedback from Self reflection
Add more examples in the tutorial questions.
Students were very eager and engaged to work with examples related to the theory. Hence, more examples can be added to the tutorial questions.
- Model internal representation, performance criteria, and computational components identifying elements of authentic problems to apply neural, fuzzy or evolutionary computation
- Create effective and efficient computational intelligence solutions to authentic problems
- Evaluate the solution to a computational intelligence problem, analysing the merits and demerits of the chosen approach
- 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 - 30% | ||||
2 - Written Assessment - 25% | ||||
3 - Written Assessment - 45% |
Alignment of Graduate Attributes to Learning Outcomes
Graduate Attributes | Learning Outcomes | |||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
1 - Knowledge | ||||
2 - Communication | ||||
3 - Cognitive, technical and creative skills | ||||
4 - Research | ||||
5 - Self-management | ||||
6 - Ethical and Professional Responsibility | ||||
7 - Leadership | ||||
8 - Aboriginal and Torres Strait Islander Cultures |
Alignment of Assessment Tasks to Graduate Attributes
Assessment Tasks | Graduate Attributes | |||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
1 - Written Assessment - 30% | ||||||||
2 - Written Assessment - 25% | ||||||||
3 - Written Assessment - 45% |
Textbooks
Computational Intelligence: Concepts to Implementations
(2007)
Authors: Russell C. Eberhart, Yuhui Shi
Morgan Kaufmann Publishers
Burlington Burlington , MA , USA
ISBN: 978-1-55860-759-0
Binding: Hardcover
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- R Studio and R
- Java SE 11
- NetBeans IDE 11
All submissions for this unit must use the referencing style: Harvard (author-date)
For further information, see the Assessment Tasks.
m.rashid@cqu.edu.au
Module/Topic
Evolutionary Computation Concepts and Paradigms
Chapter
Chapter 2 and Part of Chapter 3 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Evolutionary Algorithms: Representation
Chapter
Chapter 3 (Prescribed Textbook), and Chapter 3 and 4 from Introduction to Evolutionary Computing by A. E. Eiben and J. E. Smith
Events and Submissions/Topic
Module/Topic
Fitness Selection and Population management
Chapter
Chapter 5 from Introduction to Evolutionary Computing by A. E. Eiben and J. E. Smith
Events and Submissions/Topic
Module/Topic
Neural Networks: Components and
Terminology
Chapter
Chapter 5 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Neural Networks: Adaptation and
Learning
Chapter
Chapter 5 (Prescribed Textbook)
Events and Submissions/Topic
Assignment 1 (Due on Friday, Week 5 (8 April 2022) 11:00 pm AEST
Written Assessment Due: Week 5 Friday (8 Apr 2022) 11:00 pm AEST
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Neural Networks: Adaptation and Implementation
Chapter
Chapter 5 and 6 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Fuzzy Systems: Fuzzy Sets and Fuzzy Logic
Chapter
Chapter 7 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Fuzzy Systems: Approximate Reasoning
Chapter
Chapter 7 (Prescribed Textbook)
Events and Submissions/Topic
Assignment 2 (Due on Friday, Week 8 (6 May 2022) 11:00 pm AEST
Written Assessment Due: Week 8 Friday (6 May 2022) 11:00 pm AEST
Module/Topic
Fuzzy Decision Making
Chapter
Chapter 15 Fuzzy sets and fuzzy logic (Vol. 4) Klir, G. and Yuan, B., 1995.
Events and Submissions/Topic
Module/Topic
Fuzzy Systems: Fuzzy Controller
Chapter
Chapter 7 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Fuzzy Systems: Implementations
Chapter
Chapter 8 (Prescribed Textbook)
Events and Submissions/Topic
Module/Topic
Performance Metrics
Chapter
Chapter 10 (Prescribed Textbook)
Events and Submissions/Topic
Assignment 3 (Due on Friday, Week 12 (3 June 2022) 11:00 pm AEST
Written Assessment Due: Week 12 Friday (3 June 2022) 11:00 pm AEST
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
1 Written Assessment
In this assignment you will demonstrate your ability to apply evolutionary computing concepts to hypothetical problem, and develop a software application. This assessment task is to design, code, debug, and test using the topics covered in Weeks 1-3. Further details are in the Assignment 1 Specification document available from the Unit website.
Week 5 Friday (8 Apr 2022) 11:00 pm AEST
Submit via the Moodle Link
Week 7 Friday (29 Apr 2022)
Within 2 weeks of the due date or within 2 weeks of submission (whichever is the later)
- Appropriate application of evolutionary computation concepts to the given problem
- Correct modelling of problem solution following evolutionary computation concepts
- Effective Source code development applying correct algorithmic steps
- Effective use of good programming practice/techniques
- Rigorous testing to evaluate the correctness of the model and algorithm
- Demonstration of potential practical applications in a written report
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
- Model internal representation, performance criteria, and computational components identifying elements of authentic problems to apply neural, fuzzy or evolutionary computation
- Create effective and efficient computational intelligence solutions to authentic problems
2 Written Assessment
In this assignment you will demonstrate your ability to model a neural network to solve a hypothetical problem, and develop a software application implementing your model. This assessment task is to design, code, debug, and test using the topics covered in Weeks 4-7. Further details are available in the Assignment 2 Specification document available from the Unit website.
Week 8 Friday (6 May 2022) 11:00 pm AEST
Submit via the Moodle Link
Week 10 Friday (20 May 2022)
Within 2 weeks of the due date or within 2 weeks of submission (whichever is the later)
- Correct modelling of problem solution following artificial neural network concepts
- Effective Source code development applying correct algorithmic steps
- Effective use of good programming practice/techniques
- Rigorous testing to evaluate the correctness of the model and algorithm
- Demonstration of potential practical applications in a written report
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
- Create effective and efficient computational intelligence solutions to authentic problems
- Evaluate the solution to a computational intelligence problem, analysing the merits and demerits of the chosen approach
- Investigate the potential to enhance the model using one or more computational intelligence techniques.
3 Written Assessment
In this assignment you will demonstrate your problem solving and programming skills to apply fuzzy system concepts to a hypothetical problem, and develop a software application. This assessment task is to design, code, debug, and test using the topics covered in Weeks 7-11. Further details are in the Assignment 3 Specification document available from the Unit website.
Week 12 Friday (3 June 2022) 11:00 pm AEST
Submit via the Moodle Link
This assignment will be returned on Certification of Grades day, as is required of units of no exam
- Correct modelling of problem solution following fuzzy system concepts
- Effective Source code development for correct implementation of the model
- Effective use of good programming practice/techniques
- Rigorous testing to evaluate the correctness of the model and algorithm
- Suggestions to enhance the model combining one more computational intelligence technique to improve speed/accuracy
- Write a report on applicability of the enhanced model in real world applications that will also promote social innovation
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Research
- Self-management
- Model internal representation, performance criteria, and computational components identifying elements of authentic problems to apply neural, fuzzy or evolutionary computation
- Evaluate the solution to a computational intelligence problem, analysing the merits and demerits of the chosen approach
- Investigate the potential to enhance the model using one or more computational intelligence techniques.
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.