IFN647 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: | IFN647 |
|---|---|
| Prerequisite(s): | (IFN509 or IFQ509 or IFN580) OR (192cp in IV04) OR (admission into IV54) |
| Equivalent(s): | CAB431 |
| Assumed Knowledge: | Programming languages experience. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $5,352 |
Unit Outline: Semester 1 2025, Gardens Point, Internal
| Unit code: | IFN647 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN509 or IFQ509 or IFN580) OR (192cp in IV04) OR (admission into IV54) |
| Equivalent: | CAB431 Text Analysis and Web Search |
| Assumed Knowledge: | Programming languages experience. |
| Coordinator: | Yuefeng Li | y2.li@qut.edu.au |
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 subject will be delivered through the following means:
Lectures (2 hours) which provide the theoretical basis of the subject;
Practicals (1 hour) which allow you to apply theory to practical (industry data-driven) problems using available software tools.
Feedback on Learning and Assessment
Criteria sheets will be used to provide feedback on assessment of problem solving tasks and projects. Feedback from tutors and lecturers will be provided both directly to students through comments and/or verbally during demonstration of work, and through classroom discussions. Participation in workshops provides you with the opportunity to receive informal feedback from your tutors and peers and to self-reflect on your progress. Students will be able to meet with the lecturer/s or tutors during designated consultation times to obtain direct feedback and discuss your work and progress in the unit.
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 that answer questions, including program design, implementation, and testing.
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 a team of 2 or 3. Students will also investigate and report on the ethical implications of this work.
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.
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
- 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.IN20 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 - 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 - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: 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
IN28 Master of Artificial Intelligence
- Demonstrate advanced specialist IT knowledge in Artificial Intelligence discipline.
Relates to: Assignment 1, Exam - 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: Assignment 1, Assignment 2, Exam - 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: Assignment 1, Assignment 2, Exam - Demonstrate knowledge of Artificial Intelligence research principles and methods and their application to Artificial Intelligence focused, real-world scholarly or professional projects.
Relates to: Assignment 2 - Communicate effectively in Artificial Intelligence professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: Assignment 2 - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: Assignment 2, Exam