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

Person responsible

Jussi Tohka, Ulla Ruotsalainen

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 2 : Tuesday 12 - 14, TB222 Thursday 10 - 12, TB222	International Students

Requirements

Final examination and active participation in exercises.

Principles and baselines related to teaching and learning

-

Learning outcomes

The goal is to introduce basic methods and principles of pattern recognition.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Basics of multivariate probability and statistics, class conditional density function, Bayesian decision theory.
2.	Estimation of the parameters of the density function from training data.
3.	Nonparametric techniques for estimation of the density function and pattern classification.
4.	Algorithms for unsupervised classification.

Evaluation criteria for the course

In order to pass the course the student has to pass the exam and make at least 30% of the exercises. There will be bonus from extra exercises. To pass the exam at least half of the maximum points of the exam has to be reached. Lecture notes and exercises are enough to good grade in exam.

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
Summary of lectures	"Introduction to Pattern Recognition"	Jussi Tohka					English

Prerequisites

Correspondence of content

Additional information

Lectures and exercises in English.

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1			Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 %

Last modified	26.10.2009
Modifier	Kirsi Järnström