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Advanced Statistical Learning


Admission requirements

Students should have passed the course Basic statistical learning.
Students should have at least basic programming skills in R.


Statistical learning refers to a vast set of tools for understanding data. These techniques are being used in wide range of industries and research fields. They have been used for product and movie recommendations, predicting disease status and progression, identifying fraudulent bank transactions, and identifying genes and biomarkers associated with specific diseases, to name just a few examples.
This course will focus on extensions and generalizations of supervised learning techniques taught in the basic statistical learning course.

We will study:

  • Model selection and the bias-variance trade-off in depth.

  • Bayesian approaches to predictive modeling.

  • Extensions for mixed and longitudinal prediction (e.g.: nested, clustered, hierarchical data structures).

  • The main concepts of neural networks and their connection to other statistical learning techniques.

  • Uncertainty quantification.

  • Interpretation: Model-agnostic methods to quantify and visualize predictor variables’ effects.

Specific methods that we will study, apply and evaluate will include: Bayesian additive regression trees, Gaussian process regression, extreme gradient boosting, feed-forward neural networks, Shapley values.

Course Objectives

After the course, the student can:

  • Reason how the trade-off between bias and variance is affected by model selection and sampling strategy.

  • Adapt statistical learning techniques to complex data situations, such as nested and longitudinal data structures.

  • Infer and explain the magnitude and shape of predictor variables' effects in various predictive models using model-agnostic tools.

  • Construct basic feed-forward neural networks.

  • Model the uncertainty in various types of prediction problems using Bayesian statistical learning techniques.


You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of Instruction

Lectures and workgroups in R.

Assessment method

A written structured assignment earlier in the course and a less structured assignment at the end of the course. The final grade will be a weighted average of the grades for both reports.

Reading list

Various articles and book chapters for which links will be provided on Brightspace.


It is the responsibility of every student to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.


Marjolein Fokkema,