Skip to main content

DD2434 Machine Learning, Advanced Course 7.5 credits

A second course in machine learning, giving a broadened and deepened introduction to the area.

Choose semester and course offering

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 DD2434 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The basics of the probabilistic method.

Probabilistic modelling.

Dimensionality reduction.

Graphical models.

Hidden Markov models.


Variational Inference.

Networks in variational inference.

Intended learning outcomes

After passing the course, the student should 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 deterministic 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 do a degree project in deterministic inference methods.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Completed courses in machine learning equivalent DD2421/DD2431 and probability theory and statistics equivalent SF1901.

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

Registering for a course is counted as active participation.

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

Recommended prerequisites

No information inserted


No information inserted


No information inserted

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


  • LAB1 - Labs, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.5 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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2434

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Jens Lagergren (

Supplementary information

Grading criteria are made available when the course starts.

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