DD2432 Artificial Neural Networks and Other Learning Systems 6.0 credits

Artificiella neuronnät och andra lärande system

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

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

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.

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 Disposition

No information inserted

Literature and preparations

Specific prerequisites *

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.

Equipment

No information inserted

Literature

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

Examination and completion

Grading scale *

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

Examination *

  • LAB2 - Laboratory Assignments, 3.0 credits, Grading scale: P, F
  • TEN2 - Examination, 3.0 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.

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.

Other requirements for final grade *

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

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Erik Fransén

Pawel Herman

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 DD2432

Offered by

CSC/computational Science and Technology

Main field of study *

Computer Science and Engineering, Information Technology, Information and Communication Technology

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Erik Fransén e-post: erikf@kth.se

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.