Course - Intelligent Text Analytics and Language Understanding - TDT4310
Intelligent Text Analytics and Language Understanding
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 course consists of lectures, a set of smaller programming exercises (non-graded but obligatory), a larger programming project (graded), and a written exam. The subject thus requires an approved project report with theoretical and experimental content related to the currently most topical themes in the field. The highlights of the project should be presented to the other students (with obligatory attendance on a subset of the presentations). The lectures, presentations and exam will be given in English, unless all students speak Norwegian.
Compulsory assignments
- Assignments
Further on evaluation
A written exam (50%) and project work (50%) 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.
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