DD2432 Artificial Neural Networks and Other Learning Systems 6.0 credits

Artificiella neuronnät och andra lärande system

Please note

This course has been cancelled.

A course in computer science focusing on artificial neural networks (ANN) and other learning and self-organizing systems.

  • 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

After the course the student should be able to

  • explain the function of artificial neural networks of the Back-prop, Hopfield, RBF and SOM type
  • explain the difference between supervised and unsupervised learning
  • describe the assumptions behind, and the derivations of the ANN algorithms dealt with in the course
  • give example of design and implementation for small problems
  • implement ANN algorithms to achieve signal processing, optimization, classification and process modeling

so that the student

  • achieves an understanding of the technical potential and the advantages and limitations of the learning and self organizing systems of today
  • can apply the methods and produce applications in their working life

Course main content

The course covers algorithms which gets its computational capabilities by training from examples. There is thus no need to explicitly provide rules and instead training using measured data is performed. Learning can be done either by providing the correct answer, or be totally autonomous.

The courser also covers principles of representation of data in neural networks. The course also includes principles of hardware architectures (euro chips and neuro computers) and shows how ANN can be used in robotics. We also show applications of learning systems in areas like pattern recognition, combinatorial optimization, and diagnosis.


Single course students:

SF1604 Linear algebra, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming, DD1338 Algorithms and Data Structures, or corresponding courses.

Recommended prerequisites

The mandatory courses in mathematics, numerical analysis and computer science for D, E, and F-students or the equivalent.


Stephen Marsland: Machine Learning, an Algorithmic Perspective, 2009, CSC-Press, ISBN 1420067184


  • LAB2 - Laboratory Assignments, 3.0, grading scale: P, F
  • TEN2 - 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.

Requirements for final grade

Examination (TEN2; 3 university credits).
Laboratory assignments (LAB2; 3 university credits).

Offered by

CSC/computational Science and Technology


Erik Fransén e-post: erikf@kth.se


Erik Fransén <erikf@kth.se>

Pawel Herman <paherman@kth.se>


Course syllabus valid from: Autumn 2016.
Examination information valid from: Autumn 2007.