Pattern Recognition

Bayesian decision theory, supervised learning, parametric and non-parametric methods, linear discriminant functions, feature extraction, unsupervised learning, cluster analysis, syntactic methods.

Knowledge:
The course is an introduction to classification theory and pattern recognition. The students are provided with sufficient knowledge for designing and evaluating classifiers using proper methods for the problem at hand. And be able to read and understand methods published in the literature and evaluate and compare these with methods used in practical systems.
Skills:
Independent management of small R&D projects and contribute actively in larger projects.
General competence:
Communicate work related problems with specialists and nonspecialists.

The course is lectured at UNIK, Kjeller and you can attend the lecture in a video conferencing studio at NTNU or via streaming. Lectures, optional exercises and one project.