Course Catalog 2009-2010 International Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Ari Visa, Ulla Ruotsalainen

Implementations

No implementations

Requirements

Exam and Matlab exercises. The exercises are mandatory.

Principles and baselines related to teaching and learning

-

Learning outcomes

The aim is to deepen the understanding of pattern recognition principles and give students some ability to apply the methods on real problems. The aim is also to learn how to write in a scientific publication about the methods and the pattern classification results.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Bayesian decision theory and Bayesian parameter estimation.	Belief networks, Hidden Markov models, Linear discriminant functions
2.	Stochastic pattern classification methods.	Boltzman learning, Evolutionary methods, Genetic programming
3.	Nonmetric classification methods.	CART, tree methods in principle, Grammatical methods
4.	Algorithm-independent machine learning.
5.	Unsupervised learning and clustering, fuzzy clustering methods, component analysis methods.	Mixture densities, Hierarchical clustering, on-line clustering, graph theoretic methods, PCA and ICA

Evaluation criteria for the course

In order to pass the course the student has to complete all the exercises and get half of the maximum points from the exam. Grading is pass/fail.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	"Pattern Classification"	Duda RO, Hart PE, Stork DG			2nd edition, Wiley, 2001		English

Prerequisites

Additional information about prerequisites
Either SGN-2500 or SGN-2506 is required.

Correspondence of content

Last modified	16.06.2009
Modifier	Ari Visa