Module two: Machine Learning for Society and Culture

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Lecturer

Anna Schjøtt Hansen, a.s.hansen@uva.nl

Zarah van Hout, z.a.m.vanhout@uva.nl

Content and organization

The second course of our series to introduce interactive data analysis with Python to social science and humanities students will work through several research use cases using basic machine learning. We will use association mining to analyse communities of practice. One of the most intuitive techniques of data analysis is community detection. In the session, we will use it to explore political opinions in the US congress and how users of a social network share interest. We also employ network analysis to split up a small community network into groups and clusters before finally learning more visualisation and image analysis.

Please, note that we require you to finish the first module (insert link once published) of this course before you can start this short course.

The short course is divided into six smaller sessions organised under three themes:

Community Analysis

  • Political Communities
  • Scraping Web Data

Network Analysis

  • Creating Graphs
  • Graph Analysis

Visuals

  • Advanced Visualisation
  • Image Analysis

Level

MA and Postgraduate

Course Duration

3-5 hours

Course Type

Short Course

Participation terms

Both AIDA and non-AIDA students are encouraged to participate in this short course. To register, please send an email to Anna Schjøtt Hansen (a.s.hansen@uva.nl) and she will provide the link to the course.

If you are an AIDA Student* already, please also enrol in the same course in the AIDA system (button at the end of the page), in order for this course to be included on your AIDA Course Attendance Certificate.

*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members list (https://www.i-aida.org/about/members/).

Language

English

Modality (online/in person):

Self-study online course

Host Institution
University of Amsterdam

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