After successful completion of the course, students are able to:

- understand and explain the theoretical foundations of modern methods in natural language
processing
- consider, evaluate, and discuss both ML-based and rule-based solutions to a variety of NLP tasks
- consider, evaluate, and discuss the explainability and biases of NLP solutions
- understand and address the challenges of NLP for low-resourced languages

Tentative topics:

- Overview of tasks and methods in NLP
- Distributional models in NLP
- Explainability of NLP models
- Understanding and semantics
- Semantic parsing and representation

Introductory lectures, individual research work, group discussions

Lectures: 24h
Coursework: 35h
Project: 16h

Total: 75h

Presentation of individual research outcomes