Program
Machine Learning in Social Science Research
Methods and Applications
The course is organized into four thematic modules, one per day. Each day combines a lecture introducing core methods, an applied case study connecting the technique to a real social science problem, and from day two onwards a hands-on coding lab in R.
Time | Session | Instructor(s) |
Day 1 Foundations: machine learning in social science Tue 5 May | ||
9:45 –10:15 | Welcome and course overview | Tommaso Agasisti |
10:15–11:00 | Lecture: machine learning in social science: an overview | Ilja Cornelisz |
11:00 –11:30 | Coffee break | |
11:30–12:30 | Lecture: regularized regression: LASSO, Ridge, and Elastic Net | Chris Van Klaveren |
12:30–13:30 | Lunch | |
13:30–14.30 | Lecture: regularized regression: LASSO, Ridge, and Elastic Net | Chris Van Klaveren |
14:30–15:15 | Paper discussion: Mortality prediction; classification metrics and model validation | Ilja Cornelisz |
15:15–15:30 | Coffee break |
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15:30–16:15 | Paper discussion: mortality prediction; classification metrics and model validation | Ilja Cornelisz |
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Day 2 Education: flexible models and support vector machines Wed 6 May | ||
09:00–11:00 | Lecture: Flexible models: support vector machines and ensemble learning | Chris Van Klaveren |
11:00–11.30 | Coffee break | |
11:30–13:00 | Paper discussion: Predicting student graduation; comparing SVM and regularized regression | Ilja Cornelisz |
13:00–14:00 | Lunch | |
14:00–16:00 | R lab: Regularized regression and SVM for predicting students’ performance | Melisa Diaz Lema |
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Day 3 Criminal justice: tree-based models and random forests Thu 7 May | ||
09:00–11:00 | Lecture: Tree-based models and random forests | Chris Van Klaveren |
11:00–11:30 | Coffee break | |
11:30–13:00 | Paper discussion: Predicting recidivism using classification trees; ethical and social implications | Ilja Cornelisz |
13:00–14:00 | Lunch | |
14:00–14:30 | Paper discussion: Predicting recidivism using classification trees; ethical and social implications | Ilja Cornelisz |
14:30–16:00 | R lab: Random forests for predicting students’ experiences of bullying | Melisa Diaz Lema |
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Day 4 Synthesis: neural networks and your own research Fri 8 May | ||
09:00–11:00 | Lecture: Artificial neural networks: architecture, backpropagation, gradient descent | Chris Van Klaveren |
11:00–11:30 | Coffee break | |
11:30–13:00 | Paper discussion: Medical outcome classification using neural networks | Ilja Cornelisz |
13:00–14:00 | Lunch | |
14:00–16:00 | Research workshop and wrap-up: Applying ML methods to your own research project | All instructors |
Times are indicative and may be adjusted on-site. All R labs assume basic familiarity with R; datasets and scripts will be provided in advance.