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UID:01a0d94d-7423-71d9-8091-5b54e3354e56@www.dariah.eu
DTSTAMP:20260925T160206Z
DTSTART;VALUE=DATE:20260921
DTEND;VALUE=DATE:20260926
SUMMARY:CLARIAH-AT Summer School 2026: Machine Learning for Digital Scholar
 ly Editions
DESCRIPTION:Machine learning is increasingly shaping research in the Digita
 l Humanities\, offering powerful tools for analyzing and enriching textual
  data. In this summer school\, participants will use the Python library BE
 RTopic to explore various steps of topic modeling. Building upon BERTopic
 ’s modular architecture\, students will be introduced to essential machi
 ne learning techniques\, such as embedding\, dimensionality reduction\, an
 d 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 t
 he intersection between digital scholarly editing and Machine Learning. Af
 ter attending the school\, participants will have a basic understanding of
  machine learning algorithms and be able to assess their possible applicat
 ions as well as strengths and limitations. Participants will be able to pr
 actically use BERTopic on their own data.\n\nThis event is supported from 
 DARIAH-EU.
LOCATION:Graz\, Austria
URL:https://www.dariah.eu/events/clariah-at-summer-school-2026-machine-lear
 ning-for-digital-scholarly-editions
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