Course - Intelligent Text Analytics and Language Understanding - TDT4310
Intelligent Text Analytics and Language Understanding
Assessments and mandatory activities may be changed until September 20th.
About
About the course
Course content
The subject comprises: human languages and language processing, machine learning and statistical methods for language understanding and text analytics, text classification and deep learning, sentiment analysis and opinion mining, information access and text mining, conversational agents, machine translation, computational semantics, computational linguistic creativity.
Learning outcome
Students should have a basic and hands-on understanding of the currently used frameworks and methods for text analytics and natural language understanding, in particular the application of machine learning methods and large language models to text analytics and generation.
Learning methods and activities
The lectures, presentations and exam will be given in English, unless all students speak Norwegian.
Compulsory assignments
- Assignments
Further on evaluation
The basis for the final grade in the course is:
- Written examination (50%)
- Portfolio (40%)
- Oral presentation (10%)
The portfolio consists of three parts:
- Project plan
- Programming assignment
- Written report
Attendance is compulsory for a subset of the presentations. Further details will be provided at the start of the teaching period.
If the course is retaken, the entire portfolio must be completed again during a semester with teaching.
The re-sit examination for the written exam will take place in August. The re-sit exam may be changed to an oral format.
Recommended previous knowledge
The course is open to all students but presupposes a knowledge of Python programming at least at the level of TDT4110 ("Information Technology, Introduction"); a programming knowledge at least at the level of a 3rd year Computer Science/Informatics student is strongly recommended as is knowledge equivalent to TDT4136 ("Introduction to Artificial Intelligence"). Basic knowledge of machine learning is not required but recommended (e.g., TDT4171, "Artificial Intelligence Methods" or equivalent). No knowledge of Linguistics or of language processing is assumed.
Course materials
Information will be given at the beginning of the course.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| BBAN4040 | 4 sp | Autumn 2026 |
Subject areas
- Computer and Information Science
- Applied Information and Communication Technology
- Computer Science
- Knowledge Systems
- Information Systems
- Computer Systems
- Applied Linguistics
- Applied Linguistics
- Information Technology and Informatics
- Communication and Information Science
- Languages, Logics and Inform. Technology