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

Lectures and exercises. The subject requires an approved project report with theoretical and experimental content. The lectures will be given in English, unless all students speak Norwegian.

Compulsory assignments

• Assignments

Further on evaluation

A written exam (25%) and project work (75%) form the basis for the final grade in the course. The parts and the entire course are assigned letter grades.

If there is a re-sit examination, the examination form may change from written to oral.

When repeating the course, all partial assessments must be repeated.

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.

Required previous knowledge

TDT4110 ("Information Technology, Introduction") or equivalent.

Students lacking TDT4110 need to contact the lecturer to register for TDT4310.

See also the recommended previous courses.

Course materials

Information will be given at the beginning of the course.