IFN680 Advanced Machine Learning and Applications

Unit Outline: Semester 2 2025, Gardens Point, Internal

Overview

This is a specialisation unit in the area of computer science and data analytics. The aim of this unit is to provide you with the knowledge and skills required to design and implement modern machine learning solutions that can effectively and efficiently solve complex problems. The main advantage of intelligent systems is that they can combine the traditional computer's capacity to remember millions of facts with the human being's cognitive skills, including learning and refining the existing body of knowledge, solving problems with reasoning, helping businesses with strategic planning, diagnosing mechanical faults or human diseases, playing games, and so on. This unit will provide you with an understanding of the principles and basic techniques to understand and develop the latest machine learning techniques, as well as an understanding of the strengths and limitations of these algorithms.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Explain the fundamental theory underpinning various AI and ML techniques and algorithms, and identify their applications and limitations.
2. Work independently and in a team to implement and apply appropriate AI and ML techniques to solve specific problems.
3. Critically evaluate, synthesise, and critique the performance, strengths and limitations of AI and ML methods for given problems.
4. Communicate your results and the limitations of your models professionally with peers and the stakeholder community.

Content

Learning Approaches

This unit will be delivered with a theory-to-practice approach. Each week, you will have a 2-hour lecture, which provides you with the theoretical knowledge required for this unit. You will also have a tutorial where you will apply the theory introduced in lectures with programming to solve various problems. You will be guided through the programming problems, with consolidation of how the theory relates to the practical implementation and guided analysis of the results. For the assignments, you will be required to work individually and in pairs to solve more complex problems which will relate to industry applications with real world datasets.

Feedback on Learning and Assessment

Feedback in this unit will be provided in the following ways:

• Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
• Formative written feedback through marking rubric.
• In addition to criteria and standards in a marking rubric, comments on summative assessment will be provided.
• Generic comments will be provided to the cohort through the Canvas.

Teaching staff are available for feedback and advice in the practical sessions.

Assessment

Overview

The assessment of this unit includes two projects and a final examination. The first project is designed for you to demonstrate your understanding of the machine learning techniques introduced in this unit as well as your practical implementation skills. You will be required to implement a machine learning algorithm/s to solve a given problem and submit a report describing and analyzing your solution. The second project will require you to work in a team to apply one or more artificial intelligence techniques to solve a given problem and communicate your results and design process in a report. The final examination will allow you to demonstrate your overall knowledge that you obtain from this unit.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Project 1

In this project, you will identify and implement existing machine learning algorithms to solve an image classification problem. You will be assessed on your implementation and its performance. You must also analyse the performance of your chosen algorithms and communicate your findings in a report.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Project 2

In this project, you will design and implement various generative AI methods to create targeted data. You will detail the process of designing and refining your solution in a report.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Examination (written)

Written quiz with multiple-choice and short answer questions.

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Requirements to Study

Costs

No extraordinary charges or costs are associated with the requirements for this unit.

Resources

Resource Materials

Recommended text(s)

• Textbook
  o Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, (4th edition), 2020

Risk Assessment Statement

There are no unusual health or safety risks associated with this unit

Course Learning Outcomes

IN20 Master of Information Technology

1. Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
   Relates to: ULO1, Project 1, Examination (written)
2. Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
   Relates to: ULO3, Project 1, Project 2, Examination (written)
3. Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
   Relates to: ULO2, Project 1, Project 2
4. Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
   Relates to: ULO2, Project 1, Project 2
5. Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
   Relates to: ULO4, Project 1, Project 2

IN28 Master of Artificial Intelligence

1. Demonstrate advanced specialist IT knowledge in Artificial Intelligence discipline.
   Relates to: Project 1, Examination (written)
2. Critically analyse complex Artificial Intelligence problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
   Relates to: Project 1, Project 2
3. Integrate advanced, industry-best practice, Artificial Intelligence methods, tools and techniques to develop and implement complex Artificial Intelligence systems, processes and/or software.
   Relates to: Project 1, Project 2
4. Demonstrate knowledge of Artificial Intelligence research principles and methods and their application to Artificial Intelligence focused, real-world scholarly or professional projects.
   Relates to: Project 1, Project 2
5. Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
   Relates to: Examination (written)