Summer School Machine Learning for Digital Scholarly

Machine learning is increasingly shaping research in the Digital Humanities, offering powerful tools for analyzing and enriching textual data. In this summer school, participants will use the Python library BERTopic to explore various steps of topic modeling. Building upon BERTopic’s modular architecture, students will be introduced to essential machine learning techniques, such as embedding, dimensionality reduction, and clustering. Through practical sessions using historical texts, students will learn to apply, interpret, and critically assess these techniques. The aim is to give non-experts a high-level practical overview of how to use the BERTopic library and the essential theory behind its modules. The school is intended for both students and researchers with an interest in the intersection between digital scholarly editing and Machine Learning. After attending the school, participants will have a basic understanding of machine learning algorithms and be able to assess their possible applications as well as strengths and limitations. Participants will be able to practically use BERTopic on their own data. 

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