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DD2447 Statistical Methods in Applied Computer Science 6.0 credits

This course summarizes statistical and probabilistic methods used in applied Computer Science.

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Headings with content from the Course syllabus DD2447 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Generative models.
  • Bayesian inference.
  • Probabilistic programming.
  • Graphical models.
  • Concealed Markov models with continuous states.
  • Particle filters.
  • Monte Carlo estimation.
  • Sequential Monte Carlo.
  • Markov Chain Monte Carlo.
  • Clustering.
  • The Dirichlet process.

Intended learning outcomes

After passing the course, the student shall be able to

  • explain and justify several important methods for machine learning
  • give an account of several types of methods and algorithms that are used in the field of sample-based inference methods
  • implement several types of methods and algorithms that are used in the field based on a high-level description
  • extend and modify the methods that the course deals with

in order to be able to make a degree project in sample-based inference methods.

Literature and preparations

Specific prerequisites

Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.

Knowledge in one variable calculus, 7.5 higher education credits, equivalent to completed course SF1625.

Knowledge in probability theory and statistics, 6 higher education credits, equivalent to completed course SF1910-SF1924/SF1935.

Knowledge and skills in programming, 6 higher education credits, equivalent to completed course DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD1331/DD1337/DD100N/ID1018.

Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1326/DD1327/DD2325/ID1020/ID1021.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course.

Being registered for a course counts as active participation.

The term 'final examination' encompasses both the regular examination and the first re-examination.

Recommended prerequisites

Courses in mathematics (analysis), programming, computer science and statistics equivalent to obligatory courses on D- or F-programme.
Matlab or similar tool (Octave, R).


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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


  • INL1 - Assignment, 6.0 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.

Other requirements for final grade

Written assignments and a project (INL1; 6 higher education credits).

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

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

Please discuss with the course leader.


Jens Lagergren, e-post:

Supplementary information

In this course, the EECS code of honor applies, see:

This course overlaps with DD2420 Probabilistic Graphical Model.