Statistical learning refers to a vast set of tools for understanding data. Two classes of such tools can be distinguished: “supervised” and “unsupervised”.
- Mark de Rooij (Professor of Methodology and Statistics of Psychological Research) Tom Wilderjans (Associate Professor) Marjolein Fokkema (Assistant Professor)
Course is fully booked. Registration is no longer possible.
Supervised statistical learning involves building a statistical model for predicting an output (response, dependent) variable based on one or more input (predictor) variables. There are many areas of psychology where such a predictive question is of interest. For example, finding early markers for Alzheimer’s or other diseases, selection studies for personnel or education, or prediction of treatment outcomes.
In unsupervised statistical learning, there are only input variables but no supervising output (dependent) variable; nevertheless we can learn relationships and structures from such data using cluster analysis and methods for dimension reduction. In this course we aim to give the student a firm theoretical basis for understanding and evaluating statistical learning techniques and teach the students skills to apply statistical learning techniques in empirical research.
Upon completion of this course, students will:
- Have knowledge about the difference between explanation and prediction, about the bias-variance trade-off, and about “learners”.
- Have a good understanding of several important classes of learning techniques and be able to apply them in R to data: linear regression and classification methods, nonlinear models (splines, GAM), ensemble methods (regression/classification trees, bagging, random forest, boosting), the kernel-trick and unsupervised learning methods (dimension reduction and clustering).
- Know how to evaluate the performance of a statistical learning method by using resampling methods (validation approach, cross-validation, bootstrap) and are able to apply these methods with R to empirical data.
Please bring a laptop with you to the course with R/RStudio installed, since we will do much practical work. Also don’t forget your R-skills! (if you do not have any R-skills, please take an R-course first). Further information with respect to the preparation for the course will be send to the participants in December.
N.B. Participants must be aware that the course requires quite a lot of a priori preparation in terms of watching online lectures and reading chapters of the book.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. New York: Springer. A free copy is available online.
Registration IOPS members
IOPS members can register by sending an email to firstname.lastname@example.org.