IFQ647 Machine Learning for Natural Language Processing
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: | IFQ647 |
|---|---|
| Prerequisite(s): | IFQ580 or IFN580 or IFQ509 or IFN509 IFQ580 or IFN580 or IFQ509 or IFN509 |
| Equivalent(s): | IFN647 OR CAB431 |
| Assumed Knowledge: | Programming languages experience. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,116 |
| International unit fee | $5,616 |
Unit Outline: Session 2 2026, QUT Online, Online
| Unit code: | IFQ647 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ580 or IFN580 or IFQ509 or IFN509 |
| Equivalent: | IFN647 OR CAB431 |
| Assumed Knowledge: | Programming languages experience. |
Overview
This unit provides an understanding of the principles and techniques underlying the development of Text Analysis and Machine Learning solutions to some of the varied and complex problems that involve big data. It teaches you data preprocessing techniques to represent and analyse text, web and social media data. It also includes machine learning and its applications in Web Search, information filtering, text classification, clustering, sentiment analysis, topic modelling and generative AI techniques to understand the text data. It teaches you the methods of text analysis and machine learning algorithms for dealing with both the structured and un-structured information embedded within documents, web pages and social media platforms. This unit is motivated by the ubiquity of unstructured big data in our society and the need for future professionals and researchers to develop skills and knowledge in emerging data science approaches.
Learning Outcomes
On successful completion of this unit you will be able to:
- Compare and contrast common approaches used to represent, manipulate and analyse textual data and unstructured data.
- Apply standard models of Web search and machine learning algorithms to develop solutions to solve problems.
- Critically evaluate and justify the results of text analysis and machine learning approaches.
- Collaborate effectively in a team environment to implement a major text analysis and machine learning project.
- Critically reflect on ethical issues that arise when applying machine learning approaches.
- Professionally communicate project outcomes in both written and visual formats.
Content
The unit content covers both the theory and the practice of text analysis and machine learning algorithms. Topics covered in the unit include text data representation, natural language processing (text processing), information retrieval, language models, information filtering, machine learning algorithms, generative AI algorithms, topic modelling, sentiment analysis, social media analysis including content and structure mining methods, web analytics such as content, structure and usage mining, and recommender systems.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit provides three assessment items to enable students to understand the principles and techniques introduced in the unit and to show that they have achieved the learning objectives. The Portfolio allows students to explore a range of techniques on selected datasets, demonstrating their mastery of these approaches. The major project allows you to consider a more sophisticated problem, giving you experience with these methods on a more substantial, real world data set. Finally, the examination will allow you to show your knowledge and understanding of the theory behind these methods.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Assignment 1
A series of tasks aimed at answering questions, including the design of document and query parsers, the development of term-weighting functions and information retrieval models, as well as their implementation and testing.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Assignment 2
A major text analysis and machine learning project will be undertaken in teams of two or three. The project will involve designing baseline models as well as a new model, implementing them, and evaluating their performance using effectiveness measures and significance testing.
Students will also investigate and report on the ethical implications of this work.
The assignment consists of two deliverables:
- Final report
- Recorded demonstration
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.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Exam
A written examination, which covers the material presented in the lectures and workshops throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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
The unit's Canvas site will provide:
- Lecture notes and resources for practicals.
- Additional supporting documents and essential information.
- Assessment details and criteria sheets.
There is no prescribed textbook for this unit.
Risk Assessment Statement
No unusual risks in the offering of this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Assignment 1, Exam - 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, Assignment 1, Assignment 2, Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, Assignment 1, Assignment 2, Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO4, Assignment 2 - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Assignment 2 - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO5, Assignment 2, Exam
Unit Outline: Session 4 2026, QUT Online, Online
| Unit code: | IFQ647 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFQ580 or IFN580 or IFQ509 or IFN509 |
| Equivalent: | IFN647 OR CAB431 |
| Assumed Knowledge: | Programming languages experience. |
Overview
This unit provides an understanding of the principles and techniques underlying the development of Text Analysis and Machine Learning solutions to some of the varied and complex problems that involve big data. It teaches you data preprocessing techniques to represent and analyse text, web and social media data. It also includes machine learning and its applications in Web Search, information filtering, text classification, clustering, sentiment analysis, topic modelling and generative AI techniques to understand the text data. It teaches you the methods of text analysis and machine learning algorithms for dealing with both the structured and un-structured information embedded within documents, web pages and social media platforms. This unit is motivated by the ubiquity of unstructured big data in our society and the need for future professionals and researchers to develop skills and knowledge in emerging data science approaches.
Learning Outcomes
On successful completion of this unit you will be able to:
- Compare and contrast common approaches used to represent, manipulate and analyse textual data and unstructured data.
- Apply standard models of Web search and machine learning algorithms to develop solutions to solve problems.
- Critically evaluate and justify the results of text analysis and machine learning approaches.
- Collaborate effectively in a team environment to implement a major text analysis and machine learning project.
- Critically reflect on ethical issues that arise when applying machine learning approaches.
- Professionally communicate project outcomes in both written and visual formats.
Content
The unit content covers both the theory and the practice of text analysis and machine learning algorithms. Topics covered in the unit include text data representation, natural language processing (text processing), information retrieval, language models, information filtering, machine learning algorithms, generative AI algorithms, topic modelling, sentiment analysis, social media analysis including content and structure mining methods, web analytics such as content, structure and usage mining, and recommender systems.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
This unit provides three assessment items to enable students to understand the principles and techniques introduced in the unit and to show that they have achieved the learning objectives. The Portfolio allows students to explore a range of techniques on selected datasets, demonstrating their mastery of these approaches. The major project allows you to consider a more sophisticated problem, giving you experience with these methods on a more substantial, real world data set. Finally, the examination will allow you to show your knowledge and understanding of the theory behind these methods.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Assignment 1
A series of tasks aimed at answering questions, including the design of document and query parsers, the development of term-weighting functions and information retrieval models, as well as their implementation and testing.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Assignment 2
A major text analysis and machine learning project will be undertaken in teams of two or three. The project will involve designing baseline models as well as a new model, implementing them, and evaluating their performance using effectiveness measures and significance testing.
Students will also investigate and report on the ethical implications of this work.
The assignment consists of two deliverables:
- Final report
- Recorded demonstration
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.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Exam
A written examination, which covers the material presented in the lectures and workshops throughout the semester.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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
The unit's Canvas site will provide:
- Lecture notes and resources for practicals.
- Additional supporting documents and essential information.
- Assessment details and criteria sheets.
There is no prescribed textbook for this unit.
Risk Assessment Statement
No unusual risks in the offering of this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Assignment 1, Exam - 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, Assignment 1, Assignment 2, Exam - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, Assignment 1, Assignment 2, Exam - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others
Relates to: ULO4, Assignment 2 - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO6, Assignment 2 - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO5, Assignment 2, Exam