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Statistical Learning and Prediction


Entry requirements

Only open to master’s students in Psychology with specialisation Methodology and Statistics, master’s students Psychology, and research master’s students.


Statistical learning refers to a vast set of tools for understanding data. Two classes of such tools can be distinguished: “supervised” and “unsupervised”. 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 statistical learning techniques and teach the students skills to apply statistical learning techniques in empirical research.

Course objectives

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”;

  • Understand resampling methods and are able to apply these in data analysis;

  • Understand nonlinear and are able to fit and judge nonlinear models to data;

  • Understand ensemble methods and are able to apply these in data analysis; and

  • Understand unsupervised learning tools and can apply these to data.


For the timetables of your lectures, work groups and exams, please select your study programme in:
Psychology timetables



Students need to enroll for lectures and work group sessions.
Master’s course registration


Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date. Students who are not registered will not be permitted to take the examination.
Registering for exams

Mode of instruction

The course consists of 7 lectures of 4 hours in which we alternate between theory and practice and one question and answer session.

Assessment method

Two structured assignments and one unstructured assignment with an oral presentation. The final grade is the average of the 3 grades.

The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.

Reading list

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. New York: Springer. Free copy and online tutorials available online.

Additional suggested material:

  • Berk, R.A. (2008). Statistical learning from a regression perspective. Springer. PDF available via Leiden University Library.

  • Kuhn, M. & Johnson, K. (2013). Applied predictive modelling. Springer. PDF available via Leiden University Library.

Contact information

Dr. Tom Wilderjans