Entry requirements

Algebra MN1. Linear Algebra MN1. Analysis MN2. Computer programming MN1. Numerical analysis MN1. Probability theory MN1, or a corresponding curriculum.

Aims

The course is intended to result in basic knowledge and understanding of how different types of

learning systems can be used for problems in computer based pattern recognition like linear and

nonlinear regression, classification, feature extraction, clustering, compression and visualisation.

Applications are taken from e.g. data mining and signal analysis.

Content

Mathematical foundations of learning systems, basic concepts such as Bayesian statistical pattern

recogntion, parametric and nonparameteric methods, discriminant functions, decision surfaces,

parameter estimation, supervised and unsupervised learning, generalisation, clustering. Families of

methods like decision trees, artificial neural networks, Bayesian learning, and prototype based

learning. Applications from e.g. data mining and signal analysis.

Instruction

Lectures, lessons, seminares,

laboratory work and homework.

Assessment

Written and/or oral examination at the end of the course, homework

assignments, laboratory work.