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 2 - 2023
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 Student Feedback
Some students find it difficult to understand Particle Swarm Optimisation (PSO) and genetic programming.
A use case with sample coding will be helpful.
Feedback from Unit Coordinator Reflection
Python is a more appropriate industry-standard programming language to prepare industry-ready graduates in AI.
Introduce Python and Cloud Technology to Solve AI Problems as per unit update plan.
- 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
There are no required textbooks.
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Jupyter Notebook
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
· Introduction To Artificial Intelligence
· Fundamental Use Cases for Artificial Intelligence
Chapter
Chapter 1 and 2
Events and Submissions/Topic
Module/Topic
· Machine Learning Pipelines
· Feature Selection and Feature Engineering
Chapter
Chapter 3 and 4
Events and Submissions/Topic
Module/Topic
· Classification And Regression Using Supervised Learning
· Predictive Analytics with Ensemble Learning
Chapter
Chapter 5 and 6
Events and Submissions/Topic
Module/Topic
· Detecting Patterns with Unsupervised Learning
· Building Recommender Systems
Chapter
Chapter 7 and 8
Events and Submissions/Topic
Module/Topic
· Logic Programming
· Heuristic Search Techniques
Chapter
Chapter 9 and 10
Events and Submissions/Topic
Assessment 1 Submission: Due on Week 5 Friday (11 August 2023) 11:45 pm AEST
Assignment 1 Due: Week 5 Friday (11 Aug 2023) 11:45 pm AEST
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
· Genetic Algorithms and Genetic Programming
· Artificial Intelligence on The Cloud
Chapter
Chapter 11 and 12
Events and Submissions/Topic
Module/Topic
- Building Games with Artificial Intelligence
- Building A Speech Recognizer
Chapter
Chapter 13 and 14
Events and Submissions/Topic
Module/Topic
- Natural Language Processing
- Chatbots
Chapter
Chapter 15 and 16
Events and Submissions/Topic
Assessment 2 Submission: Due on Week 8 Friday (8 September 2023) 11:45 pm AEST
Assignment 2 Due: Week 8 Friday (8 Sept 2023) 11:45 pm AEST
Module/Topic
- Sequential Data and Time Series Analysis
- Image Recognition
Chapter
Chapter 17 and 18
Events and Submissions/Topic
Module/Topic
· Neural Networks
· Deep Learning with Convolutional Neural Networks
Chapter
Chapter 19 and 20
Events and Submissions/Topic
Module/Topic
- Recurrent Neural Networks and Other Deep Learning Model
- Creating Intelligent Agents with Reinforcement learning
Chapter
Chapter 21 and 22
Events and Submissions/Topic
Module/Topic
Review and Assignment Completion
Chapter
Events and Submissions/Topic
Assessment 3 Submission: Due on Week 12 Friday (6 October 2023) 11:45 pm AEST
Assignment 3 Due: Week 12 Friday (6 Oct 2023) 11:45 pm AEST
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Unit coordinator:
Dr. Md Mamunur Rashid
School of Engineering & Technology
CQUniversity Melbourne, 120 Spencer Street, Melbourne 3000
P +61 3 9616 0425 | X 50425 | E m.rashid@cqu.edu.au
Textbooks: Prescribed
Artificial Intelligence with Python second edition (2020) Authors: Artificial Intelligence with Python ISBN: 9781839219535
1 Written Assessment
Assignment 1 is designed to reinforce the contents taught in Week 1 to Week 4. Assignment 1 is an individual assessment and should be submitted in Week 5. In this assessment students have to write python code to solve the given problem(s). Students have to choose specific AI tool(s) to solve the problem(s) and have to justify the reason of choosing the specific AI tool(s). This assessment contributes to 35% of the total marks. This assessment will address the following unit learning outcomes: Apply industry tools to solve AI problems and critique business cases for AI systems against social and ethical frameworks.
Week 5 Friday (11 Aug 2023) 11:45 pm AEST
Submit online via Moodle link
Week 7 Friday (1 Sept 2023)
Within two weeks of submission
The students will be marked based on their ability to:
- Choose the correct AI tool and justifying the reason of this choice
- Writing the correct Python code
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks
The detailed marking criteria can be accessed on the unit Moodle. Please make sure to read through the marking criteria carefully before submitting your work.
- 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
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
2 Written Assessment
Assignment 2 is designed to reinforce the contents taught in Week 5 to Week 7. Assignment 1 is an individual assessment and should be submitted in Week 8. In this assessment students have to write python code to solve the given problem(s). Students have to choose specific AI tool(s) to solve the problem(s) and have to justify the reason of choosing the specific AI tool(s). This assessment contributes to 25% of the total marks. This assessment will address the following unit learning outcomes: Apply industry tools to solve AI problems and critique business cases for AI systems against social and ethical frameworks.
Week 8 Friday (8 Sept 2023) 11:45 pm AEST
Submit online via the Moodle link
Week 10 Friday (22 Sept 2023)
Within two weeks of submission
The students will be marked based on their ability to:
- Choose the correct AI tool and justifying the reason of this choice
- Writing the correct Python code
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks
The detailed marking criteria can be accessed on the unit Moodle. Please make sure to read through the marking criteria carefully before submitting your work.
- 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.
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Self-management
3 Written Assessment
Assignment 3 is an individual task where students have to develop python code to solve the given real-world problem(s). Students have to choose specific AI tool to solve the given problem and have to justify the reason of choosing the specific AI tool. This assessment will address the following unit learning outcomes: select Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges; apply industry tools to solve AI problems and critique business cases for AI systems against social and ethical frameworks.
Week 12 Friday (6 Oct 2023) 11:45 pm AEST
Submit online via the Moodle link
Feedback and marks for this assessment will be released after the certification date as this unit does not have an exam.
The students will be marked based on their ability to:
- Ability to choose Artificial Intelligence (AI) techniques to solve authentic problems including social innovation challenges
- Justifying the reason of this choice
- Develop the correct Python code
- Apply industry tools to solve AI problems
- Critique business cases for AI systems against social and ethical frameworks
The detailed marking criteria can be accessed on the unit Moodle. Please make sure to read through the marking criteria carefully before submitting your work.
- 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.
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Research
- Self-management
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?
