Natural Language Processing in Business Research

This graduate-level course provides a rigorous and self-contained treatment of deep learning (DL) for natural language processing (NLP), focusing on the architectures and methods driving current research on large language models (LLMs). While foundational concepts, such as tokens and embeddings, are covered to ensure a shared baseline, the course quickly advances to recent topics including encoder-based models (BERT and beyond), multitask learning, and generative LLMs.

The course is structured across three dimensions: methodological depth, hands-on coding, and applied research. Students develop a thorough understanding of key NLP architectures, gain practical programming skills to fine-tune models and work with generative AI tools, and explore how these methods can be integrated into business and economics research through dedicated application sessions and research presentations. A central goal is to equip graduate students not only to understand current NLP methods, but to independently apply and critically reflect on them within their own research.

Anmeldefrist: 23. August 2026