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SF2935 Modern Methods of Statistical Learning 7.5 credits

About course offering


For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Application code



For course offering

Autumn 2024 Start 26 Aug 2024 programme students


Pierre Nyquist (


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Course coordinator

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Headings with content from the Course syllabus SF2935 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course presents an overview of the most important methods of the modern theory of statistical learning. Topics covered include supervised learning with a focus on classification methods, support vector machines, artificial neural networks, decision trees, boosting, bagging and methods of unsupervised learning with focus on K-means clustering and nearest neighbours. This course focuses primarily on the practical aspects of statistical learning. Computer-aided project work with a variety of datasets forms the essential learning activity.

Intended learning outcomes

For the methods presented in the course, the student shall possess both theoretical and practical understanding of how the methods work, which ones to choose for a given problem and how to implement rudimentary versions of them. Computer-aided projects form an essential learning activity.

To pass the course the student shall be able to

  • formulate and apply methods for supervised learning,
  • formulate and apply methods for unsupervised learning,
  • apply mathematical theory to analysis and explain properties of methods in statistical learning,
  • design and implement methods in statistical learning for different tasks.

Literature and preparations

Specific prerequisites

  • English B / English 6
  • Completed basic course in probability theory and mathematical statistics (SF1918, SF1922 or equivalent).

Recommended prerequisites

Numerical methods (SF1544, SF1545 or similar), differential equations (SF1633, SF1683 or similar), probability and statistics (SF2940 or similar), regression analysis (SF2930 or similar).


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • TENA - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • ÖVN1 - Assignments, 3.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The written exam deals with concepts.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study


Education cycle

Second cycle

Add-on studies

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Pierre Nyquist (