The ability to process and analyse large volumes of text is essential in today's information-driven world. This course introduces technologies for the analysis of textual documents, covering both foundational and modern approaches. It begins with the basics of information retrieval and text classification, then advances to deep learning methods in natural language processing, including the use of pre-trained large language models (LLMs). Emphasis will be placed on the theoretical foundations of the methods and algorithms, while practical exercises will provide students with valuable hands-on experience.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Explain, implement, and evaluate fundamental information retrieval methods, and analyse their effectiveness on real-world document collections.
- Design and assess machine learning pipelines for text classification, including using cross validation to perform model selection.
- Explain, implement, and experiment with embedding-based text representations and their application in downstream NLP tasks.
- Explain, implement, train, and evaluate commonly used neural network architectures for text processing, including recurrent neural networks and transformer models.
- Explain the principles underlying large language models, including training objectives, architectures, fine-tuning strategies, and critically assess their capabilities and limitations in applied settings.
- Evaluate and interpret the performance of document analysis methods using appropriate metrics, and effectively communicate the findings.
Indicative Assessment
- Quizzes (15) [LO 1,2,3,4,5,6]
- Assignments (30) [LO 1,2,3,4,5,6]
- Final Examination (55) [LO 1,2,3,4,5,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Lectures and tutorial/laboratory sessions plus self study to a total of 130 hours.
Inherent Requirements
None
Requisite and Incompatibility
Prescribed Texts
None
Preliminary Reading
The following reference books will be used.
- Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft), 2025.
- Introduction to Information Retrieval, C.D. Manning, P. Raghavan and H. Scutze, Cambridge University Press, 2008.
Assumed Knowledge
Programming ability in C, C++, Java or Python, basic knowledge in probability theory, calculus, and linear algebra, at an undergraduate-level.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2026 | $5520 |
- International fee paying students
| Year | Fee |
|---|---|
| 2026 | $7020 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 10087 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |