Introduction to Machine Learning with R and Rstudio

About

This course explores Machine Learning (ML) and Artificial Intelligence (AI) for social sciences, starting with a refresher on R and RStudio. You’ll learn supervised and unsupervised techniques, including classification, regression, clustering, and dimension reduction methods. The course also covers ML applications for unstructured data, such as image analysis with neural networks and text classification with LDA and BERT. Through theory and hands-on practice, you’ll gain the skills to apply ML in social science research.

Course leader

Dr M.R.M. Maurice Vergeer

Target group

Bachelor, Advanced Bachelor, Master, PHD, Postdoc, Professional.

Admission requirements
Participants should have a basic level of understanding statistics and R and Rstudio.

Course aim

Learning objectives
1. Identify and evaluate suitable Machine Learning (ML) techniques for various research problems in social science.
2. Apply a range of ML techniques, including classification, regression, clustering, and dimension reduction, to structured and unstructured datasets.
3. Develop hands-on experience in implementing ML methods using R and RStudio.
4. Understand the distinctions between supervised and unsupervised learning approaches and their applications.
5. Analyze and interpret the results of ML models to draw meaningful conclusions for research purposes.

Fee info