Course Profile¶
Objectives¶
This course aims to provide students with a foundational understanding of Natural Language Processing (NLP) concepts and techniques. By the end of this course, students will be equipped to preprocess text data, engineer relevant features, and apply basic machine learning models on text data. Additionally, students will gain insights into advanced NLP concepts like large language models and generative AI, along with their practical applications.
Contents¶
- NLP basics
- Text preprocessing
- NLP pipelines
- Feature engineering
- Vector space models
- Probabilistic models
- Sequence and attention models
- Generative AI and large language models
- Practical implications
Assessment¶
The course is graded based on a written 90-minute exam at the end of the semester. To be admitted to the exam, it is required to
- complete all assignments and
- give a presentation on an NLP topic.
The presentation and assignments are ungraded.
Course Language¶
All course materials are provided in English. Lectures will be held in German unless we have international students. In this case, the lectures will also be in English.
Course Format¶
The course will be held in a hybrid format. There will be a few in-person lectures throughout the semester, but most of the course will be held online.