Machine Learning of Natural Language processing (2025-2026)

This microcredential has been carved out as a balanced selection of contemporary modules from the MA in Digital Text Analysis that targets industry professionals who are interested in learning about modern machine learning and natural language processing. With an emphasis on project and team work, we develop practical applications on textual data, as well as solutions for the many issues that remain open in the field.

This micro-credential focuses on the following learning outcomes.

1. The participant will have theoretical knowledge about the history and the main frameworks of Machine Learning.
2. The participant will have theoretical knowledge of the main machine learning algorithms and paradigms and will study some of them in depth.
3. The participant will be able to develop machine learning pipelines and set up their own machine learning experiments using Python modules.
4. The participant will have theoretical and practical knowledge of machine learning using neural networks.
5. The participant will understand the fundamental problems and approaches in automatic Natural Language Processing and know its history as a subfield within Linguistics and Artificial Intelligence.
6. The participant will have insight into the basic algorithms developed within NLP for morphological, syntactic, semantic, and discourse processing.
7. The participant will acquire hands-on experience with software for text categorization, language understanding, translation, and generation.

• All courses extensively rely on weekly, hands-on homework assignments, ensuring the acquisition of new, practical insights on a regular basis. The homework takes the form of engaging assignments on real-world datasets that challenge the students to apply the theoretical concept introduced during the interactive class sessions.
• The final evaluation of all three courses depends on project work, the goal and finality of which can be determined by the individual students, in close correspondence with the course teachers.
• An attractive feature of the evaluation of the NLP course (in the 2nd semester) is that students will participate in an ongoing shared task in the field.