Courses

Natural Language Processing

Fall 6 ECTS

Students will get familiar with common concepts and tasks in NLP. They will learn how to evaluate NLP technologies and frameworks and gain practical experience during the laboratory sessions. They will get a deep understanding of the methods underlying modern transformer-based language models, but at the same time get an understanding of how the field of NLP went from rule-based to more and more data-driven methods. They will also gain basic skills needed for conducting NLP research, such as reading papers, critically discussing results and finding possible ways of improvement. Lastly, they will learn to reflect on the impact of modern NLP technologies on society.




Reflections on AI for Communication

Fall 3 ECTS

The course will cover - among others - the following topics:

  • Introduction to Natural Language Processing (NLP) and Artificial Intelligence (AI)
  • Ethical issues of AI and AI regulations

The aim of this course is to make students critically reflect on the use of AI for communication purposes, to understand the many opportunities it brings, but also to review the attested negative effects, and to discuss possible positive and negative long-term impact on society. In order for students to be able to critically reflect on the societal impact of AI for communication, the course will start with an introduction to NLP, including Machine Learning (ML) and Large Language Models (LLMs) using excerpts from a textbook on NLP (Jurafsky and Martin, 2025). The focus will be on text-based models, but we will also touch upon other modalities such as vision. These introductions will make students understand the machinery behind AI tools that are currently widely used, such as AI chatbots, machine translation, and content generation tools using generative AI. After these introductions, the main topics in AI ethics, such as bias, issues related to data, transparency, privacy as well as initiatives for AI regulation will be discussed. Especially, this latter part of the course requires active student involvement and critical reflection. Students give presentations and co-lead class discussions with help from the lecturer.




NLP for Business and Finance

Spring 3 ECTS

The course will cover - among others - the following topics:

  • Introduction to Natural Language Processing (NLP)
  • Introduction to Machine Learning
  • Basic Python programming
  • Overview of NLP applications most relevant to business and finance



NLP for Business and Finance — Project

Spring 3 ECTS

This course gives students the opportunity to work in interdisciplinary teams on projects that implement NLP solutions for challenges and tasks from business and other organizations. It includes an introduction to interdisciplinary teamwork, project work with real-world data provided by business partners, intermediary progress updates, and a final presentation and report.

Prerequisite: Basic Python programming and machine learning/NLP, or successful completion of the NLP for Business and Finance course.

Over the past few years, thanks to the impressive capabilities of large language models (LLMs), the automatic analysis and generation of textual data has found rapid uptake in business and other organizations. This course invites business partners to provide students with actual challenges and data, giving them experience with real-world complexity, exposure to the world of work, and a network that may benefit their future careers.




Responsible Use of AI in Communication

Spring 3 ECTS

The course will cover — among others — the following topics:

  • Introduction to Natural Language Processing (NLP)
  • Overview of NLP applications most relevant to tourism and digital fashion communication
  • Conversational AI
  • Prompt Engineering
  • Ethical issues of AI
  • AI regulations



Advanced Topics in NLP

Spring 3 ECTS

This seminar-based course explores advanced topics in NLP. At the start of the semester, selected topics and a list of research papers are shared with students. Each student presents one scientific work and leads a discussion on the selected paper; all students are expected to read each paper and contribute to discussions of its merits and weaknesses. Possible topics include (but are not limited to) multilingual NLP, and cognitive aspects of NLP including creativity, reasoning, and argumentation.

The main aim is to give students a deeper understanding of advanced NLP topics. Through presentations and paper discussions, students learn to critically assess prior work, propose extensions of current methods, and identify avenues for future research.