Statistical learning models are tools for understanding and predicting data. The course introduces supervised and unsupervised learning models for regression and classification problems that have a wide range of applications in health data science. Topics include techniques for training and testing models, model selection and regularization (ridge regression and lasso), illustrated with linear and logistic regression models, nonlinear models such as k-nearest neighbors, trees and random forests, basic elements and principles of neural networks, cluster analysis, and dimension reduction with principal component analysis.

For each modeling technique, there is a short theoretical introduction, followed by a practical implementation in R and the interpretation of the resulting R output. Complementary exercise sheets are provided for students to gain hands-on experience with the modeling techniques; students will present their solutions to each other. In the end of the course, students will apply their knowledge by presenting and discussing academic research papers from various fields of the health sciences (e.g., health psychology, health economics, and medicine) that contextualize the modeling techniques covered.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd ed.). Springer.