ETE387 · 3 credits · Advanced level (A1N) · Datavetenskap
Natural Language Processing (NLP)
Active · next start HT 2026
100%
passed the latest regular sitting
UPG1 · 14 Jan 2026 · 9 students
72%
passed the regular sittings, all sittings over the last 5 years
UPG1 · 25 students
Hand-in or lab
UPG1 Online quizzes
- G
- U
Main rounds
72% passed · 25 students
- 100%9
- 56%16
Later rounds
88% passed · 8 students
- 88%8
- Pass a later round: 88% (8 students), compared with 72% (25 students) on the ordinary exam
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Computer laboratory work
- G
- U
Main rounds
50% passed · 16 students
- 86%7
- 22%9
Later rounds
71% passed · 7 students
- 71%7
- Pass a later round: 71% (7 students), compared with 50% (16 students) on the ordinary exam
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%4
- 100%4
About the course
Aim
On completion of the course, the student should be able to:
- explain central concepts, models and algorithms in natural language processing
- implement NLP methods and apply them to realistic problems
- evaluate NLP components and systems using appropriate methods
Content
Natural Language Processing (NLP) enables computers to work with human language. In this course, you will explore the core models and algorithms that underpin modern NLP, with a strong emphasis on practical experience. The course focuses particularly on large language models (LLMs) and the development of chatbots.
- Introduction to natural language processing
- Tokenisation and embeddings (e.g. word2vec)
- Language models based on the Transformer architecture (e.g. GPT, BERT)
- Development of LLMs and chatbots: data, pretraining, finetuning
- Program libraries and datasets
- Evaluation methods
- Current research and development
- Examiner
- Marco Kuhlmann
- Scheduled hours
- 6 h
- Main field of study
- Datavetenskap