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University of Gothenburg
Natural language processing and large language models
The course gives an introduction to machine learning models and architectures used in modern natural language processing (NLP) systems, including large language models (LLMs). This is a technical course with a research angle that covers the technical solutions underlying modern NLP technologies and large language m…
- Higher education
- Information unavailable
- 2 November 2026
- Information unavailable
- Information unavailable
- 50 %
Overview
The course gives an introduction to machine learning models and architectures used in modern natural language processing (NLP) systems, including large language models (LLMs). This is a technical course with a research angle that covers the technical solutions underlying modern NLP technologies and large language models.
Admission scores
Entry requirements
To be eligible to the course, the student should have a Bachelor's degree in any subject. In addition, the course requires: - 7\.5 credits of courses in programming or equivalent, - a course including probability and statistics, such as Statistical Methods for Data Science or Mathematical Statistics and Discrete mathematics, - a course in machine learning, such as Applied Machine Learning, Algorithms for Machine Learning and Inference, or Statistical Learning for Big Data. Applicants must prove knowledge of English: English 6/English level 2 or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.
The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.
Source, measure and data quality
- Source
- Skolverket Susa-navet
- Period
- 2026-11-02
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.gu.dit249.86134.20262
- Last checked
- 2026-09-23T10:36:42.164498+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
Programme content
Study structure
Application and important dates
- Programme or course starts
- Programme or course ends
Salary and salary distribution
Common occupations after graduation
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Geographical background
Previous upper-secondary schools and programmes
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About the provider
Sources and data quality
Education facts for the selected offering come from Skolverket Susa-navet.
Retrieved . Published . Times are shown in Swedish local time.
Source identity and publication version
- Publication version
- 8e217193-f5fa-4778-b085-a4521fd03e8d
- Education identity in the source
- i.uoh.gu.dit249.86134.20262
- Offering identity in the source
- e.uoh.gu.dit249.86134.20262
- Education-form source code
- HS
- Education code in the source
- DIT249
- Change time according to the source
- 2026-02-17T15:13:04
The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.