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FDD3434 Machine Learning, Advanced Course 7.5 credits

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FDD3434 (Autumn 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Machine learning is the science of algorithms that improve their performance by learning from experience; most often in the form of data with or without labelled examples. Machine learning algorithms are used within a large number of application fields. Independently of the field, a developer of such algorithms need to have a systematic understanding of how a given assignment can be formulated as a machine learning problem. The aim of this course is to give you this systematic understanding. We will present a number of machine learning algorithms and statistical modelling algorithms. But above all, you will learn how the different algorithms are constructed, how they relate to one another and when they are applicable in theory and in practice.

Intended learning outcomes

After the course, the students should be able to

*explain, derive and implement a number of models of supervised and unsupervised learning,

*analytically demonstrate how different models and algorithms relate to one another,

*explain strengths and weaknesses for different models and algorithms,

*choose appropriate model or strategy for a new machine learning task.

More specifically, regarding methodologies the student should be able to

*explain the EM-algorithm and identify problems where it is applicable,

*explain the terminology for Bayesian networks and topic models and apply these on realistic amounts of data,

*explain and derive boosting algorithms and design new boosting algorithms with different cost functions,

*explain and implement methods for learning of feature representations from various types of data.

Literature and preparations

Specific prerequisites

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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, 7.5 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.

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