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Introduction to machine learning for social science research

In this workshop, Dr Iulia Cioroianu will provide a brief overview of the most common machine learning algorithms used in social science research.

  • 22 May 2019, 3.00pm to 22 May 2019, 4.00pm BST (GMT +01:00)
  • 1 West North, 3.24, University of Bath
  • This event is free
Two graphs side by side
K-means clustering is one of the simplest and most popular unsupervised machine learning algorithms. Pictured here is a comparison of K-Means clustering results with 3 and 4 clusters.

This workshop will introduce a range of machine learning techniques and provide an overview of the learning resources available to social science researchers who are interested in developing the skills needed to apply these methods to a range of substantive research questions.

The topics that Dr Iulia Cioroianu will cover in the session are:

  1. Introduction: What is machine learning?

  2. Supervised learning

    • Classification
    • Evaluating and selecting models
  3. Unsupervised learning

    • Clustering
    • Topic Models
    • Scaling
  4. Overview of available learning resources and tools

Speaker profiles

Dr Iulia Cioroianu is a Prize Fellow in the Institute for Policy Research (IPR) at the University of Bath.

She holds a PhD in Political Science from New York University and was an associate lecturer and researcher in the Q-Step Centre for quantitative social sciences at the University of Exeter before joining the IPR.

Iulia is a social data scientist who studies the effects of social media and online information exposure on political competition and polarisation using natural language processing and quantitative text analysis, machine learning and survey experiments.

Who should attend

This workshop is only for Department of Politics, Languages & International Studies staff and postgraduate research students.


This venue has disabled ramp access.


1 West North, 3.24 University of Bath Claverton Down Bath BA2 7AY United Kingdom


If you have any questions, please contact us.