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Statistical Learning

Statistical learning is the statistics side of machine learning, and it has applications many areas, from finance and medicine to handwriting recognition. This course focuses on supervised learning, where a set of training data is used to infer a function that can then be applied to new data.

The course treats basic principles and methods of statistical learning, classification and prediction. As part thereof the following concepts are studied: discriminant analysis, cross validation, regularization through shrinkage and smoothing, decision- and regression trees, and support vector machines and methods of clustering.

You may also be interested in the course MT7039 Unsupervised learning.

  • Course structure

    The course consists of two modules, theory and hand-in assignments.

    Teaching format

    Teaching consists of lectures, exercise sessions and supervision in computer rooms.


    Assessment takes place through a written exam, and written and oral presentation of the hand-in assignments.


    A list of examiners can be found on

    Exam information

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.

    Course literature Department of Mathematics

  • More information

    New student
    During your studies

    Course web

    We do not use Athena, you can find our course webpages on

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