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SF2957 Statistical Machine Learning 7.5 credits

About course offering

For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Target group

 Available for all master program students as long as it can be included in your programme.

Part of programme

Master's Programme, Applied and Computational Mathematics, åk 2, Optional

Master's Programme, Applied and Computational Mathematics, åk 2, DAVE, Conditionally Elective


P2 (7.5 hp)


28 Oct 2024
13 Jan 2025

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Application code



For course offering

Autumn 2024 Start 28 Oct 2024 programme students


Henrik Hult (


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Course coordinator

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Headings with content from the Course syllabus SF2957 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course presents an overview of advanced methods of statistical machine learning. Topics covered include classical and Bayesian decision theory, deep learning for regression and classification, Gaussian processes for regression and classification, clustering, reproducing kernel Hilbert spaces, reinforcement learning, and computational methods in machine learning. Computer-aided projects with a variety of datasets forms an essential learning activity.

Intended learning outcomes

After completion of the course, the student shall be able to:

  •  formulate and apply statistical decision theory
  •  formulate and apply advanced methods in statistical machine learning
  •  design and implement advanced methods in statistical machine learning for applications

Literature and preparations

Specific prerequisites

  • English B / English 6
  • Completed basic course in numerical analysis (SF1544, SF1545 or equivalent)
  • Completed basic course in probability theory and statistics (SF1922, SF1914 or equivalent)
  • Completed advanced course in probability theory (SF2940 or equivalent)

Recommended prerequisites

Completed courses SF2935 Modern methods in statistical learning or equivalent, and SF2955 Computer intensive methods in statistics or equivalent.


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Various books and lecture notes presented on the course web page.

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


  • PRO1 - Project, 3.0 credits, grading scale: P, F
  • TENA - Written exam, 4.5 credits, grading scale: A, B, C, D, E, FX, 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.

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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Henrik Hult (