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.
Additional source review: awaiting review. Published wording is shown below.
Some conditions may need additional evidence or clarification. These are prototype interpretations, not ANU decisions or degree-completion checks.
Requisite and Incompatibility
To enrol in this course you must be enrolled in Master of Computing (Advanced) OR have completed 6 units of: (COMP6240/2400 OR COMP6260/1600 OR COMP6442/2100) AND have completed 6 units of: (COMP6730/1730 OR COMP6710/1110). Incompatible with COMP4650 and COMP6490 and COMP6990.
Referenced entries (8)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP6240 · 2027
- COMP6260 · 2027
- COMP6442 · 2027
- COMP6730 · 2027
- COMP6710 · 2027
- COMP4650 · 2027 · saved source not linked
- COMP6490 · 2027
- COMP6990 · 2027 · saved source not linked
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.
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | Second Semester | 10087 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |
| 2028 | Second Semester | 10848 | 24 Jul 2028 | 31 Jul 2028 | 31 Aug 2028 | 27 Oct 2028 | In Person | N/A |
Complete offering text
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. The list of offerings for future years is indicative only. Class summaries, if available, can be accessed by clicking on the View link for the relevant class number. 2027 2028 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 Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 10848 24 Jul 2028 31 Jul 2028 31 Aug 2028 27 Oct 2028 In Person N/A
Source evidence (2 saved copies)
Matching copies share this record. Their original file locations and checksums are retained.
assets/Courses/Artificial Intelligence/8000/Document Analysis/Document Analysis - ANU.html
Published ANU page · saved page URL
SHA-256: 6b9f317a275ca21eb459d08b81e9f4c1b1c5f97833758d405762183545447748assets/Courses/Data Science/8000/Document Analysis/Document Analysis - ANU.html
Published ANU page · saved page URL
SHA-256: 5a6e5aa25c28d01750667ef083cd875922c3d2559b0c495afa1101481c04d81d