Artificial neural networks and other learning systems, 6 credits

Current/forthcoming course: ann14.

To find previous courses, old material etc, please consult the old webpage.

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

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.

Eligibility

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. English B, or equivalent.

Prerequisites

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

Literature

To be announced at least 4 weeks before course start at course web page. Previous year: Stephen Marsland: Machine Learning, an Algorithmic Perspective 2009, CSC-Press, ISBN 1420067184.

Examination

LAB2 - Laboratory Assignments, 3.0 credits, grade scale: P, F
TEN2 - Examination, 3.0 credits, grade scale: A, B, C, D, E, 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/Computer Science

Contact

Erik Fransén e-post: erikf @ csc.kth.se

Examiner

Erik Fransén <erikf @ csc.kth.se>

Add-on studies

DD2431 Machine learning.

Version

Course plan valid from: Autumn 09.
Examination information valid from: Autumn 07.