DD2431 Machine Learning 6.0 credits


Please note

This course has been cancelled.

Advanced course in computer science where the students learn to use methods from machine learning, computational intelligence and soft computing.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
    Information Technology
    Information and Communication Technology
  • Grading scale

    A, B, C, D, E, FX, F

Last planned examination: spring 20.

At present this course is not scheduled to be offered.

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

  • explain the principles, advantages, limitations such as overfitting and possible applications of machine learning
  • identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision problems.

Course main content

The course is intended for both undergraduate and 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 probability theory insofar they are relevant to machine learning. The following topics in machine learning and computational intelligence are covered in detail

-nearest neighbour classifier
-decision trees
-bias and variance trade-off
-probabilistic methods
-Bayesian learning
-support vector machines
-artificial neural networks
-ensemble methods
-dimensionality reduction
-subspace methods.


Single course students: 90 university credits including 45 university credits in Mathematics and/or Information Technology and the courses SF1604 Linear algebra, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or equivalent.

Recommended prerequisites

DD1321, DD1340 DD1343, DD1344, DD1346 or corresponding.


Will be announced on the course webpage before the course starts.


  • LAB1 - Laboratory Work, 3.0, grading scale: P, F
  • TEN1 - Examination, 3.0, grading scale: A, B, C, D, E, FX, F

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Offered by

CSC/Robotics, Perception and Learning


Atsuto Maki, e-post: atsuto@kth.se


Atsuto Maki <atsuto@kth.se>

Örjan Ekeberg <ekeberg@kth.se>

Add-on studies

Please discuss with the instructor.


Course syllabus valid from: Autumn 2016.
Examination information valid from: Autumn 2008.