| Coordinator | Tom Wilderjans, Marjolein Fokkema |
| Tom Wilderjans, Marjolein Fokkema | |
| Venue | Leiden University |
| Mandatory/elective | Elective |
| ECTS |
2 |
| Registration | secretariaat&iops.nl |
| Fee | IOPS members: free Affiliated members: 400 Euros (these will be billed by the Leiden University) |
| Abstract | 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 and evaluating statistical learning techniques and teach the students skills to apply statistical learning techniques in empirical research. . |
| Examination | Preliminary study and active participation |
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