The lecture introduces key concepts and methods of Natural Language Processing (NLP) and demonstrates how they can be applied in real-world, complex application domains. It covers fundamental steps of text preprocessing, techniques for text representation such as vectorization, and the functioning of modern language models.
Through core NLP tasks such as text classification, information extraction, and sequence labeling, students learn to apply appropriate evaluation metrics and critically assess model performance. In practice-oriented projects, they apply the acquired techniques to scenarios such as automated fact-checking or medical decision support.
A particular focus is placed on interpreting model outputs and assessing their reliability, essential skills in sensitive application areas. Basic knowledge of Python and machine learning is recommended.
The lecture is presence only and if you want to participate come to the first lecture: starting from 21.10. (weekly), 4-6pm, room ER 164!
- Trainer/in: Nils Feldhus
- Trainer/in: Vera Schmitt
- Trainer/in: Veronika Solopova
- Trainer/in: Jing Yang