Artificial neural networks and other learning systems, 6 credits

Current/forthcoming course: ann12.

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.

Aim

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

Syllabus

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.

Prerequisites

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

Follow-up

Requirements

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

Required reading

To be announced at course start. Previous year: Fausett, Fundamentals of Neural Networks, Prentice Hall.

Other

Usual examiner is Erik Fransen.

*Replaces 2D1432.