zum Inhalt springen
Summer School: Deep Learning for language analysis

The “Summer School on Deep Learning for Language Analysis” addresses students and doctoral candidates from linguistics and digital humanities, as well as other fields that are involved with machine learning techniques. The first part of the summer school consists of an introduction to machine learning techniques for the analysis of natural languages, with a special emphasis on deep learning approaches. In the second part, participant will explore more specific applications in three parallel sessions: Argument-Mining in written texts, optical character recognition in handwritten texts (OCR or HTR, respectively), and audio-mining (e.g. the detecting of patterns in speech data).

Weitere Informationen finden Sie hier.


 

DHCon an der Universität zu Köln

Beim ersten Games und Media Showcase-Event des Instituts für Digital Humanities präsentieren Studierende aus dem Bereich Digital Humanities und Medien Ergebnisse aus Lehrveranstaltungen des Jahres 2018/2019.

Die Seite zu dieser Veranstaltung finden Sie hier.


 

Late Summer School: Machine Learning for language Analysis

Kurse und Inhalte:

  • Session 1: Learning Machine Learning.
    Lecturer: Nils Reiter (IMS, University of Stuttgart)
  • Session 2a (Fri/Sat, parallel session): Machine Learning with Audio and Speech Data.
    Lecturers: Abdullah Abdullah, download course material here
    David Laqua (Fraunhofer IAIS), download course material [1] [2] [3] [whiteboard pics]
  • Session 2b (Fri/Sat, parallel session): Deep Learning with Text Data.
    Lecturers: Johanna Binnewitt, Valmir Etemi, and Julia Kappes (Institute for Digital Humanities, University of Cologne)

 

Machine Learning Day 2017

This one-day symposium highlighted research activities at the University of Cologne applying machine learning techniques to vastly different fields — all the way from quantum matter to biological systems to economic and societal systems. Pedagogical talks werecomplemented by ample time for interactions amongst the participants. Organized by Prof. Trebst and Prof. Tresch in collaboration with the competence area 3 “Quantitative Modeling of Complex Systems”, the 2017 Machine Learning Day was a continuation of annual events such as the Computational Science Days and the Big Data Symposium.