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FDD3447 Statistical Methods in Applied Computer Science 6.0 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FDD3447 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Basic statistical concepts and basic probability theory.

Generative models.

Bayesian inference.

Directed graphical models.

Undirected graphical models.

Exactly inference for graphical models.

State space models.

Particle filters.

Monte Carlo estimation.

Sequential Monte Carlo.

Markov Chain Monte Carlo.


The Dirichlet process.

Intended learning outcomes

The student should, on completion of the course, be able to:

explain and justify several important machine learning methods,

account for a number of types of methods and algorithms that are used in the field and implement them by means of the book, as well as expand and modify them

evaluate the application of the methods in new contexts critically and design new applications, follow research and development in the area.

Literature and preparations

Specific prerequisites

For non-program students, 90 credits are required, of which 45 credits have to be within mathematics or information technology. Furthermore, English B or the equivalent is required.

Recommended prerequisites

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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

P, F


  • EXA1 - Examination, 6.0 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.

Examination takes place in the form of homework and project.

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

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Jens Lagergren (

Postgraduate course

Postgraduate courses at EECS/Computational Science and Technology