Overview
Artificial Intelligence (AI) is becoming an important part of software development. Neural networks and Deep Learning are the main contributors to the recent advances in applications of Artificial Intelligence. Deep Learning enables computers to learn complicated concepts by building them out of a hierarchy of simpler ones. Deep Learning techniques have been successfully applied to a broad field of applications such as computer vision, image and video recognition, natural language processing, and medical diagnosis. This unit introduces you to the fundamentals of Deep Learning and how it can solve problems in many areas. In this unit, you will learn the architecture of neural networks and algorithms, including the latest Deep Learning techniques. You will learn to develop conventional neural networks such as multilayer perceptrons, and convolutional neural networks. You will use software to train and deploy neural networks. You will also identify practical applications of Deep learning by exploring recent case studies.
Details
Pre-requisites or Co-requisites
Pre-requisite: COIT20277 Introduction to Artificial Intelligence
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 3 - 2024
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
The current weight of Assignment 3 (Applied Project) is 35%. This should be increased as it is a larger project.
We will evaluate and adjust assessments weight in the future offerings of the unit.
Feedback from Self-reflection
Large Language Models (LLMs) represent a key direction in the latest AI developments. Since COIT29225 is part of the AI minor, incorporating one week of content introducing LLMs seems essential.
We will add one week content (ppt file) of introduction of LLMs.
- Formulate a neural network and deep learning problem applying the concepts and theory of classical and deep learning techniques
- Design deep learning solutions to problems in pattern recognition and image analysis
- Build a software application implementing neural networks using a high level programming language
- Evaluate the performance of the deep learning techniques used in the software application
- Investigate the application of intelligent systems in socially innovative applications.
The Skills Framework for the Information Age (SFIA) standard covers the skills and competencies related to information and communication technologies. SFIA defines levels of responsibility and skills. SFIA is adopted by organisations, governments and individuals in many countries. SFIA is increasingly being used when developing job descriptions and role profiles. SFIA can be used by individuals for creating personal skills profile. The Australian Computer Society (ACS) recognises the SFIA and provides MySFIA for ACS members to build a skills profile.
This unit contributes to the following workplace skills as defined by SFIA 7 (the SFIA code is included):
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 | 5 | |
| 1 - Practical Assessment - 30% | |||||
| 2 - Practical Assessment - 35% | |||||
| 3 - Project (applied) - 35% | |||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1 - Knowledge | |||||
| 2 - Communication | |||||
| 3 - Cognitive, technical and creative skills | |||||
| 4 - Research | |||||
| 5 - Self-management | |||||
| 6 - Ethical and Professional Responsibility | |||||
| 7 - Leadership | |||||
| 8 - First Nations Knowledges | |||||
| 9 - 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 | 9 | |
| 1 - Practical Assessment - 30% | |||||||||
| 2 - Practical Assessment - 35% | |||||||||
| 3 - Project (applied) - 35% | |||||||||
Textbooks
There are no required textbooks.
Additional Textbook Information
Anaconda3 2019.10 or 2020.02
Jupyterlab, Jupyter notebook, SPyder latest version (All these can be installed via Anaconda navigator)
Python 3.7 or higher
TensorFlow and Keras latest version
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Python 3.7 or higher
- TensorFlow and Keras
- Anaconda3 2019.10 or 2020.02
All submissions for this unit must use the referencing style: Harvard (author-date)
For further information, see the Assessment Tasks.
a.jayal@cqu.edu.au
Week 1
Begin Date: 04 Nov 2024Module/Topic
Advanced APIs of Python Programming - Numpy, Scipy, Matplotlib, Pandas, and Data Visualization
Chapter
Lecture Notes
Events and Submissions/Topic
Week 2
Begin Date: 11 Nov 2024Module/Topic
An Introduction to Neural Networks
Chapter
Textbook Chapter 1
Events and Submissions/Topic
Week 3
Begin Date: 18 Nov 2024Module/Topic
Machine Learning with Shallow Neural Networks
Chapter
Textbook Chapter 2
Events and Submissions/Topic
Week 4
Begin Date: 25 Nov 2024Module/Topic
Deep Neural Networks and Learning Algorithms (1)
Chapter
Textbook Chapter 3
Events and Submissions/Topic
Week 5
Begin Date: 02 Dec 2024Module/Topic
Deep Neural Networks and Learning Algorithms (2)
Chapter
Textbook Chapters 3&4
Events and Submissions/Topic
Week 6
Begin Date: 09 Dec 2024Module/Topic
Unsupervised Learning Algorithms
Chapter
Lecture Notes
Events and Submissions/Topic
Week 7
Begin Date: 16 Dec 2024Module/Topic
Convolutional Neural Networks (1)
Chapter
Textbook Chapter 8 - Part 1
Events and Submissions/Topic
Vacation Week
Begin Date: 23 Dec 2024Module/Topic
Chapter
Events and Submissions/Topic
Vacation Week
Begin Date: 30 Dec 2024Module/Topic
Chapter
Events and Submissions/Topic
Week 8
Begin Date: 06 Jan 2025Module/Topic
Convolutional Neural Networks (2)
Chapter
Textbook Chapter 8 - Part 2
Events and Submissions/Topic
Week 9
Begin Date: 13 Jan 2025Module/Topic
Deep Learning - practices and applications
Chapter
Textbook Chapter 8 - Part 3 & Lecture Notes
Events and Submissions/Topic
Week 10
Begin Date: 20 Jan 2025Module/Topic
Restricted Boltzmann Machines
Chapter
Textbook Chapter 6
Events and Submissions/Topic
Week 11
Begin Date: 27 Jan 2025Module/Topic
Recurrent Neural Networks
Chapter
Textbook Chapter 7& Lecture Notes
Events and Submissions/Topic
Week 12
Begin Date: 03 Feb 2025Module/Topic
Recent Advances in AI Technology
Chapter
Lecture notes and supplementary material
Events and Submissions/Topic
Exam Week
Begin Date: 10 Feb 2025Module/Topic
Exam Week
Chapter
Events and Submissions/Topic
Unit Coordinator: Dr Ambi Jayal
Email: a.jayal@cqu.edu.au
Textbooks
Prescribed
Neural Networks and Deep Learning
Edition: 1st (2018)
Authors: Charu C. Aggarwal
Springer
Gewerbestrasse Gewerbestrasse , Cham , Swissland
ISBN: 978-3-319-94462-3
View textbooks at the CQUniversity Bookshop
1 Practical Assessment
For this assignment, you are required to showcase your understanding from weeks 1 through 6 by addressing real-world challenges using machine learning algorithms. You will design and implement the software solutions that need to use python programming skills, including the use of the built-in python advanced libraries (such as NumPy, SciPy, Matplotlib and Scikits, etc.) for performing various tasks including data visualization Further specifics about the assignment can be found on the course webpage.
Please note:re-attempts of this assessment are not allowed.
Week 6 Friday (13 Dec 2024) 11:45 pm AEST
Online via Moodle
Week 8 Friday (10 Jan 2025)
Online via Moodle
- Understanding the problem
- Build a solution with appropriate learning algorithm.
- Show the performance evaluation using appropriate metrics.
- Write a report and demonstrate how to write research papers.
- Formulate a neural network and deep learning problem applying the concepts and theory of classical and deep learning techniques
- Design deep learning solutions to problems in pattern recognition and image analysis
- Knowledge
- Communication
- Cognitive, technical and creative skills
2 Practical Assessment
In this assignment, we'll evaluate your grasp of the concepts taught during weeks 4-7. You'll encounter two challenges that can be addressed using regression and classification techniques. It's essential to comprehend the issues and select the suitable algorithm for resolution using the provided benchmark dataset. More specifics can be found on the course webpage.
Please note:re-attempts this assessment are not allowed.
Week 10 Friday (24 Jan 2025) 11:45 pm AEST
Online via Moodle
Week 12 Friday (7 Feb 2025)
Online via Moodle
- Read the dataset properly and clean up if needed.
- Solution design for the problems.
- Use appropriate libraries and show good programming practices.
- Cross validation and classification accuracy
- Report to present the key findings and learning.
- Formulate a neural network and deep learning problem applying the concepts and theory of classical and deep learning techniques
- Design deep learning solutions to problems in pattern recognition and image analysis
- Build a software application implementing neural networks using a high level programming language
- Investigate the application of intelligent systems in socially innovative applications.
- Knowledge
- Communication
- Cognitive, technical and creative skills
- Research
- Ethical and Professional Responsibility
- Leadership
3 Project (applied)
For this assignment, you're tasked with creating an application based on the content from weeks 8-12. Leveraging Convolutional Neural Networks (CNN) and Deep Learning, which are commonly used for image-related challenges, this assignment will allow you to tackle industry-standard issues and demonstrate your problem-solving capabilities in real-world scenarios. Utilise well-recognised libraries such as TensorFlow and Keras, and feel free to employ cloud technologies for your solutions. Conclusively, document the accuracy comparisons and, if possible, surpass the existing accuracy levels. Then, compile your findings into a research paper for submission. Comprehensive guidelines can be found in the Project Specification document on the course's webpage.
Exam Week Friday (14 Feb 2025) 11:45 pm AEST
Online via Moodle
Online via Moodle
- Problem analysis and choose correct algorithms.
- Correct design of the CNN and learning parameters
- Correct use of machine learning libraries
- Document the findings and key contributions
- Good programming practice and produce industry standard code.
- Build a software application implementing neural networks using a high level programming language
- Evaluate the performance of the deep learning techniques used in the software application
- Investigate the application of intelligent systems in socially innovative applications.
- Knowledge
- Cognitive, technical and creative skills
- Research
- Self-management
- Ethical and Professional Responsibility
- Leadership
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?
