IFB320 Generative AI
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IFB320 |
|---|---|
| Prerequisite(s): | (IFB220 or DSB102) and (CAB201 or (IFB104 and MXB103)) (CAB201 can be studied concurrently with IFB320) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 2027, Gardens Point, Internal
| Unit code: | IFB320 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB220 or DSB102) and (CAB201 or (IFB104 and MXB103)) (CAB201 can be studied concurrently with IFB320) |
Overview
Generative AI is one of the most significant developments in machine learning research in recent years, and one that has received enormous attention in industry, academia and among the general public. Many industries are being transformed by advancements in this space and it is the subject of significant media attention.
This unit is a technically-focused jump into the world of large language models and other forms of Generative AI, through both theory and practical applications.
Learning Outcomes
On successful completion of this unit you will be able to:
- Create applications that use GenAI to automate tasks for various IT problems.
- Critically evaluate the effectiveness of GenAI systems.
- Analyse and explain the societal and ethical impact of GenAI, including its effects on Aboriginal and Torres Strait Islander communities.
- Develop lifelong learning skills, including the use of AI tools to facilitate independent and continuous learning and keep pace with GenAI advancements
- Communicate the capabilities and limitations of GenAI to technical and non-technical audiences
Content
In this unit you will learn about:
- The context of Generative AI in the modern world, with a focus on their impact on various business applications
- Recent advancements and high-profile uses of Generative AI
- How Generative AI can be leveraged to drive innovation and create new business opportunities
- Tokenisation approaches and the important role they play in the context of language models
- Multi-head self-attention, positional encoding, and the transformer architecture
- Generative adversarial networks and diffusion models
- The techniques used for training large language models, including pretraining and fine-tuning
- Optimisation approaches for large language models, including quantisation, distillation, and pruning
- Advanced agent-based uses of Generative AI
- Multimodal models, image and audio generation
- Social and ethical issues surrounding Generative AI, including the data they are trained on and how they are used, and on their impact on Aboriginal and Torres Strait Islander communities
- The impact Generative AI training and inference has on energy use and carbon emissions
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You should expect to spend, on average, 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as in independent study to consolidate your learning.
The formal contact of the unit will consist of a lecture and a laboratory-based practical.
Pre-recorded materials and readings will be made available on a weekly basis and you are expected to be familiar with the material before the weekly lecture. These will provide a basic framework of understanding. The lecture will be your opportunity to ask questions, and will feature a mix of discussions, demonstrations and deeper interrogations of the material. The practicals will then further reinforce this, providing deeper knowledge via hands-on experience and consideration of real-world applications. The practicals include structured step-by-step exercises as well as opportunities for self-directed exploration of Generative AI technologies.
Feedback on Learning and Assessment
You will receive feedback on completed and marked assignments via individual comments on assignment scripts. The unit coordinator and lecturers will be available via email or in person (by appointment) to answer questions.
Assessment
Overview
The assessment for this unit consists of two major projects.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: AI Agent
In this assessment item you will create a piece of software - a Generative AI agent designed to operate within a particular domain and handle queries and tasks in response to natural language instructions from users. It will be tested on a wide range of inputs, some of which will not be provided to you in advance. Time will be allocated during laboratory sessions to check-in on the progress of this assessment task and to provide verbal formative feedback and guidance on your progress.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Inference Task
In this assessment, you will solve a complex inference task using generative models. You will be tasked with delivering code or a model to work with a provided dataset. You will also write a report in which you will reflect on the advantages and disadvantages of such a solution, and its potential implications for businesses, and society.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
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.
Resources
All required learning resources to support your learning in this unit will be made available via your Canvas unit site.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: AI Agent, Inference Task - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: AI Agent - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: AI Agent, Inference Task - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Inference Task - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Inference Task - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: AI Agent
IN01 Bachelor of Information Technology
- Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: ULO2, AI Agent, Inference Task - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: ULO1, AI Agent - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: ULO4, AI Agent - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: ULO5, Inference Task - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: ULO1, AI Agent - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: ULO3, Inference Task
Unit Outline: Semester 1 2027, Online
| Unit code: | IFB320 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB220 or DSB102) and (CAB201 or (IFB104 and MXB103)) (CAB201 can be studied concurrently with IFB320) |
Overview
Generative AI is one of the most significant developments in machine learning research in recent years, and one that has received enormous attention in industry, academia and among the general public. Many industries are being transformed by advancements in this space and it is the subject of significant media attention.
This unit is a technically-focused jump into the world of large language models and other forms of Generative AI, through both theory and practical applications.
Learning Outcomes
On successful completion of this unit you will be able to:
- Create applications that use GenAI to automate tasks for various IT problems.
- Critically evaluate the effectiveness of GenAI systems.
- Analyse and explain the societal and ethical impact of GenAI, including its effects on Aboriginal and Torres Strait Islander communities.
- Develop lifelong learning skills, including the use of AI tools to facilitate independent and continuous learning and keep pace with GenAI advancements
- Communicate the capabilities and limitations of GenAI to technical and non-technical audiences
Content
In this unit you will learn about:
- The context of Generative AI in the modern world, with a focus on their impact on various business applications
- Recent advancements and high-profile uses of Generative AI
- How Generative AI can be leveraged to drive innovation and create new business opportunities
- Tokenisation approaches and the important role they play in the context of language models
- Multi-head self-attention, positional encoding, and the transformer architecture
- Generative adversarial networks and diffusion models
- The techniques used for training large language models, including pretraining and fine-tuning
- Optimisation approaches for large language models, including quantisation, distillation, and pruning
- Advanced agent-based uses of Generative AI
- Multimodal models, image and audio generation
- Social and ethical issues surrounding Generative AI, including the data they are trained on and how they are used, and on their impact on Aboriginal and Torres Strait Islander communities
- The impact Generative AI training and inference has on energy use and carbon emissions
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You should expect to spend, on average, 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as in independent study to consolidate your learning.
The formal contact of the unit will consist of a lecture and a laboratory-based practical.
Pre-recorded materials and readings will be made available on a weekly basis and you are expected to be familiar with the material before the weekly lecture. These will provide a basic framework of understanding. The lecture will be your opportunity to ask questions, and will feature a mix of discussions, demonstrations and deeper interrogations of the material. The practicals will then further reinforce this, providing deeper knowledge via hands-on experience and consideration of real-world applications. The practicals include structured step-by-step exercises as well as opportunities for self-directed exploration of Generative AI technologies.
Feedback on Learning and Assessment
You will receive feedback on completed and marked assignments via individual comments on assignment scripts. The unit coordinator and lecturers will be available via email or in person (by appointment) to answer questions.
Assessment
Overview
The assessment for this unit consists of two major projects.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: AI Agent
In this assessment item you will create a piece of software - a Generative AI agent designed to operate within a particular domain and handle queries and tasks in response to natural language instructions from users. It will be tested on a wide range of inputs, some of which will not be provided to you in advance. Time will be allocated during laboratory sessions to check-in on the progress of this assessment task and to provide verbal formative feedback and guidance on your progress.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Inference Task
In this assessment, you will solve a complex inference task using generative models. You will be tasked with delivering code or a model to work with a provided dataset. You will also write a report in which you will reflect on the advantages and disadvantages of such a solution, and its potential implications for businesses, and society.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
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.
Resources
All required learning resources to support your learning in this unit will be made available via your Canvas unit site.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: AI Agent, Inference Task - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: AI Agent - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: AI Agent, Inference Task - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Inference Task - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Inference Task - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: AI Agent
IN01 Bachelor of Information Technology
- Critically analyse and conceptualise complex IT challenges and opportunities using modelling, abstraction, ideation and problem-solving to generate, evaluate and justify recommended solutions.
Relates to: ULO2, AI Agent, Inference Task - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: ULO1, AI Agent - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: ULO4, AI Agent - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: ULO5, Inference Task - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: ULO1, AI Agent - Critically reflect, using a human-centric approach, on the social, cultural, ethical, privacy, legal, sustainability, and accessibility issues shaping the development and use of IT, including respecting the perspectives and knowledge systems of Aboriginal and Torres Strait Islander peoples, ensuring IT solutions empower and support people with disabilities, and fostering inclusive and equitable digital technologies that serve diverse communities.
Relates to: ULO3, Inference Task