Courses of the IT centre
These courses are usually held in German.
Audiovisuelle Medienkompetenz: Videoproduktion
Date: 21.09. – 02.10.20, Mon–Fri 09:30–16:30 h, ZIV SR, Scharnhorststr. 100
Lecturer: Olaf Glaser
Einführung in MariaDB/MySQL
Date: 06.04. – 13.07.20, Mon 10–12 h, ZIV Computer-Lab 3, Einsteinstr. 60
Lecturer: Martin Leweling
High Performance Computing
Date: 24.08. – 28.08.20, Mon–Fri 10–16 h, SRZ 102, Orléans-Ring 12
Lecturer: Holger Angenent
Programmieren in Java
Date: 08.04. – 15.07.20, Wed 14–16 h, ZIV Computer-Lab 3, Einsteinstr. 60
Lecturer: Dr. Marcel Wunderlich
Programmieren in Python
Date: 21.09. – 25.09.20, Mon–Fri 09–16 h, ZIV Computer-Lab 3, Einsteinstr. 60
Lecturer: Dr. Markus Blank-Burian
Publizieren mit LateX
Date: 31.08. – 04.09.20, Mon–Fri 09–15 h, ZIV Computer-Lab 3, Einsteinstr. 60
Lecturers: Dr. Damian Bucher, Christian Schild
Vektorbasierte Illustration
Date: 09.04. – 16.07.20, Thu 10–12 h, ZIV Computer-Lab 3, Einsteinstr. 60
Lecturer: Christopher Burgholz
Courses open to all who are interested
AI@WWU - A practical introduction to Al theory and techniques for interdisciplinary research
Date: Mo 16–19 h (start: 6 April 2020), VSH 2019 (Aula)
Lecturer: Jun.-Prof. Dr. Benjamin Risse
Short Summary:
AI and in particular machine learning (ML) tools become more and more accessible due to easy to
use programming environments (esp. Python) and libraries (esp. Tensorflow and Pytorch). In order
to apply these powerful tools for a variety of research projects, some basic understanding is
required to tackle data preparation, visualisation and successful ML algorithm usage. In this
course we will (1) teach AI and machine learning basics (70% of the course) and (2) apply these
techniques to custom problems and custom data provided by the participants (30% of the
course). In particular, we will introduce several state of the art deep learning algorithms like CNNs,
LSTMs and Autoencoder. The entire course will be interactive and the participants will implement
and use all presented techniques in pre-configured test environments.
Goal:
- Teach AI and machine learning basics (70%)
- Work on own data and project (30%)
Organisation:
- 10 sessions á 3 hours
- Each Session would be 40-60 minutes presentation and ~ 2 hours of practical coding
- Programming will be done in Python and Tensorflow2
- Programming environment will be provided by pre-installed Jupyter Notebooks (software, own
hardware has to be used)
• Jupyter Notebooks allow interactive worksheets (only some code passages need to be filled
in, the result is then plotted interactively)
- Limited to 50 participants max.
Participants allowed: University staff from all disciplines (PhDs, PostDocs, Group Leaders, etc.)
- Participants should be interested in learning the basic theory and practical usage of AI (in particular deep learning) algorithms
- All disciplines are welcome (i.e. science and humanities with interest in quantitative methods)
- Own datasets / problem sets are welcome; if these can be used needs to be evaluated in the beginning of this course
Please register at b.risse(at)uni-muenster.de.
Workshop des Career Service: Digitalisierung als Laufbahnchance für Geistes- und Sozialwissenschaftler*innen
This course is held in German.
Date: Thu, 28.05.2020, 10.15–16.45 h and Fri, 29.05.2020, 10.15–16.45 h; Career Service, Schlossplatz 3, Seminarraum 2
Lecturers: Dr. Mareike Menne, Andreas Eimer
Courses of the departments
These courses are usually held in German.
Übung: Digitales Arbeiten zur Konsumgeschichte: Münsterländische Nachlassinventare des 18. Jahrhunderts
Date: Wed 8–10 h, F 072
Lecturer: Henning Bovenkerk
Übung: Methoden der digitalen Geschichtswissenschaft. Texte, Daten, Netze
Date: Mon 14–16 h, F 042
Lecturer: Jun.-Prof. Dr. Christine Fertig
Übung: Hacking Mühlhausen - Digitale Sozialtopographie einer spätmittelalterlichen Stadt (Summer School mit Exkursion)
Lecturer: Colin Arnaud, Dr. Daniel Stracke
Hauptseminar: Künstliche Intelligenz als anthropologische Herausforderung (Ethik)
Date: Thu 16-18 h, ETH 203
Lecturer: Prof. Dr. Anne Käfer
Seminar: Digitale Literaturwissenschaft
Date: Fri, 17.04., 8.05., 29.05, 14–20 h, BB 302 , BB 107, BA 006
Lecturer: Priv.-Doz. Dr. Christian Sieg
Übung: GIS-Grundkurs
Lecturers: Prof. Dr. Angela Schwering, Julian Kuhlmann
V/Ü (Master): Advanced GIS Methods in Digital Cartography
Date: Mon 18–20 h, StudLab 125, Heisenbergstr. 2
Lecturer: Sven Harpering
Übung: Python in GIS
Date: Tue 8–10 h and Thu 8–10 h, StudLab 126
Lecturer: Jun.-Prof. Dr. Judith Verstegen
Seminar: 'R' you ready? Datenanalyse für Sozialwissenschaftler*innen
Date: Tue 16–18 h, SCH 100.301
Lecturer: Phillip Hocks
Übung: Analyse und Visualisierung ökologischer Daten in R
Date: Wed 12–14 h, StudLab 126
Lecturer: Lilian-Maite Lezama-Valdes
Seminar: Digitale Kommunikation
Date: Fri 10–12 h, VSH 06
Lecturer: Dr. Katharina König
Seminar: Medienkompetenz als Teil der Lehrerprofessionalität - ein Forschungsseminar zur Erkundung digitaler Kompetenzen angehender LehrerInnen
Date: Mon 12–14 h, BB 208
Lecturer: Dr. Kris-Stephen Besa
Seminar: Zahl und Sinn - Digitalisierung, Quantifizierung und Metrisierung als Realabstraktionen sozialer Ordnung
Date: Tue 16–18 h, SCH 121.555
Lecturer: Prof. Dr. Joachim Renn