CQUniversity Unit Profile
COIT20277 Introduction to Artificial Intelligence
Introduction to Artificial Intelligence
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The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

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

Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
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).

Offerings For Term 1 - 2021

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).

Class and 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.

Class Timetable

Bundaberg, Cairns, Emerald, Gladstone, Mackay, Rockhampton, Townsville
Adelaide, Brisbane, Melbourne, Perth, Sydney

Assessment Overview

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

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.

Previous Student Feedback

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 Student evaluation

Feedback

Students appreciated the fact that this unit introduced the three areas of computational intelligence - evolutionary, neural, and fuzzy.

Recommendation

Maintain the current topic and contents.

Feedback from Student evaluation

Feedback

Provide more explanations in tutorials for the example source code given.

Recommendation

Inform teaching team to provide walk through of some of the example solutions.

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 Learning Outcomes, Assessment and Graduate Attributes
N/A Level
Introductory Level
Intermediate Level
Graduate Level
Professional Level
Advanced Level

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

Alignment of Assessment Tasks to Graduate Attributes

Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Written Assessment - 30%
2 - Written Assessment - 25%
3 - Written Assessment - 45%
Textbooks and Resources

Textbooks

Prescribed

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

You will need access to the following IT resources:
  • CQUniversity Student Email
  • Internet
  • Unit Website (Moodle)
  • R Studio and R
  • Java SE 11
  • NetBeans IDE 11
Referencing Style

All submissions for this unit must use the referencing style: Harvard (author-date)

For further information, see the Assessment Tasks.

Teaching Contacts
Nahina Islam Unit Coordinator
n.islam@cqu.edu.au
Schedule
Week 1 Begin Date: 08 Mar 2021

Module/Topic

Evolutionary Computation Concepts and Paradigms

Chapter

Chapter 2 and Part of Chapter 3

Events and Submissions/Topic

Week 2 Begin Date: 15 Mar 2021

Module/Topic

Evolutionary Algorithms: Representation

Chapter

Chapter 3, and Chapter 3 and 4 from Introduction to Evolutionary Computing by A. E. Eiben and J. E. Smith

Events and Submissions/Topic

Week 3 Begin Date: 22 Mar 2021

Module/Topic

Fitness Selection and Population management

Chapter

5 Introduction to Evolutionary Computing by A. E. Eiben and J. E. Smith

Events and Submissions/Topic

Week 4 Begin Date: 29 Mar 2021

Module/Topic

Neural Networks: Components and
Terminology

Chapter

Chapter 5

Events and Submissions/Topic

Week 5 Begin Date: 05 Apr 2021

Module/Topic

Neural Networks: Adaptation and
Learning

Chapter

Chapter 5

Events and Submissions/Topic

Written Assessment Due: Week 5 Friday (9 Apr 2021) 11:55 pm AEST
Vacation Week Begin Date: 12 Apr 2021

Module/Topic

Chapter

Events and Submissions/Topic

Week 6 Begin Date: 19 Apr 2021

Module/Topic

Neural Networks: Adaptation and Implementation

Chapter

5 and 6

Events and Submissions/Topic

Week 7 Begin Date: 26 Apr 2021

Module/Topic

Fuzzy Systems: Fuzzy Sets and Fuzzy Logic

Chapter

Chapter 7

Events and Submissions/Topic

Week 8 Begin Date: 03 May 2021

Module/Topic

Fuzzy Systems: Approximate Reasoning

Chapter

Chapter 7

Events and Submissions/Topic

Written Assessment Due: Week 8 Friday (7 May 2021) 11:55 pm AEST
Week 9 Begin Date: 10 May 2021

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

Week 10 Begin Date: 17 May 2021

Module/Topic

Fuzzy Systems: Fuzzy Controller

Chapter

7

Events and Submissions/Topic

Week 11 Begin Date: 24 May 2021

Module/Topic

Fuzzy Systems: Implementations

Chapter

Chapter 8

Events and Submissions/Topic

Week 12 Begin Date: 31 May 2021

Module/Topic

Performance Metrics

Chapter

Chapter 10

Events and Submissions/Topic

Written Assessment Due: Week 12 Friday (4 June 2021) 11:55 pm AEST
Review/Exam Week Begin Date: 07 Jun 2021

Module/Topic

Chapter

Events and Submissions/Topic

Exam Week Begin Date: 14 Jun 2021

Module/Topic

Chapter

Events and Submissions/Topic

Term Specific Information

Unit Coordinator: Dr. Nahina Islam

                            email: n.islam@cqu.edu.au

                        


Assessment Tasks

1 Written Assessment

Assessment Title
Written Assessment

Task Description

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.


Assessment Due Date

Week 5 Friday (9 Apr 2021) 11:55 pm AEST

Submit via the Moodle Link


Return Date to Students

Week 7 Friday (30 Apr 2021)


Weighting
30%

Assessment Criteria

  1. Appropriate application of evolutionary computation concepts to the given problem
  2. Correct modelling of problem solution following evolutionary computation concepts
  3. Effective Source code development applying correct algorithmic steps
  4. Effective use of good programming practice/techniques
  5. Rigorous testing to evaluate the correctness of the model and algorithm
  6. Demonstration of potential practical applications in a written report


Referencing Style

Submission
Online

Submission Instructions
Submit only one zip file (.zip) containing the source code files (.java) and document file (.doc or .docx).

Learning Outcomes Assessed
  • 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


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Self-management

2 Written Assessment

Assessment Title
Written Assessment

Task Description

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.


Assessment Due Date

Week 8 Friday (7 May 2021) 11:55 pm AEST

Submit via the Moodle Link


Return Date to Students

Week 10 Friday (21 May 2021)


Weighting
25%

Assessment Criteria

  1. Correct modelling of problem solution following artificial neural network concepts
  2. Effective Source code development applying correct algorithmic steps
  3. Effective use of good programming practice/techniques
  4. Rigorous testing to evaluate the correctness of the model and algorithm
  5. Demonstration of potential practical applications in a written report


Referencing Style

Submission
Online

Submission Instructions
Submit a zip file (.zip) containing the source code files (.java) and the report document file (.doc or .docx).

Learning Outcomes Assessed
  • 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.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Self-management

3 Written Assessment

Assessment Title
Written Assessment

Task Description

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 8-11. Further details are in the Assignment 3 Specification document available from the Unit website.


Assessment Due Date

Week 12 Friday (4 June 2021) 11:55 pm AEST


Return Date to Students

Exam Week Friday (18 June 2021)


Weighting
45%

Assessment Criteria

  1. Correct modelling of problem solution following fuzzy system concepts
  2. Effective Source code development for correct implementation of the model
  3. Effective use of good programming practice/techniques
  4. Rigorous testing to evaluate the correctness of the model and algorithm
  5. Suggestions to enhance the model combining one more computational intelligence technique to improve speed/accuracy
  6. Write a report on applicability of the enhanced model in real world applications that will also promote social innovation


Referencing Style

Submission
Online

Submission Instructions
Submit a zip file (.zip) containing the source code files (.java) and the report document file (.doc or .docx).

Learning Outcomes Assessed
  • 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.


Graduate Attributes
  • Knowledge
  • Communication
  • Cognitive, technical and creative skills
  • Research
  • Self-management

Academic Integrity Statement

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.

What can you do to act with integrity?