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FDD3431 Graduate Course in Machine Learning 6.0 credits

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.


For course offering

Spring 2024 Start 16 Jan 2024 programme students

Application code


Headings with content from the Course syllabus FDD3431 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course is intended for graduate students in computer science and related fields such as engineering and statistics. The course addresses the question how to enable computers to learn from past experiences. It introduces the field of machine learning describing a variety of learning paradigms, algorithms, theoretical results and applications.

It introduces basic concepts from statistics, artificial intelligence, information theory and control theory insofar they are relevant to machine learning. The following topics in machine learning and computational intelligence are covered in detail

-concept learning

-decision tree learning

-Bayesian learning

-artificial neural networks

-instance based learning

-computational learning theory

-evolutionary algorithms

-reinforcement learning.

Intended learning outcomes

The objective of this course is to give students

·      basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence

·      a practical knowledge of machine learning algorithms and methods

so that they will be able to

·      understand the principles, advantages, limitations and possible applications of machine learning

·      identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision problems.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

No information inserted


No information inserted


To be announced at least 4 weeks before course start at course web page. Previous year: T. Mitchell, Machine Learning, McGrawHill was used.

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.

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 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

Please discuss with the instructor.


Atsuto Maki,

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning