SGN-2506 Introduction to Pattern Recognition, 4 cr., Fall 2005

[Lectures] [Exercises] [Goal and contents] [Requirements] [Exams] [Literature] [Links]

Lectures (28h):

Time and place: Monday 14-16 p.m., Room TB222
First lecture: 30.08.2004
Lecturer: Edisson Albán, TE309

Lectures are given in English.

More information

Material

To be posted soon.

Exercises (14h):

First exercises are on week 38. Tuesday 14-th of September 2005. You can freely select between groups.

Exercise groups in English:

Group A: On Tuesday, 09:15 to 10:00 a.m., room TB214.

Group B: On Wednesday, 15:15 to 16:00 p.m., room TC161.

Exercise groups in Finnish:

Group A: On Tuesday, 10:15 to 11:00 a.m., room TB214.

Group B: On Wednesday, 14:15 to 15:00 a.m., room TC161.

A third exercise group could be arranged, if required.

Requirements:

At least 10% of the exercises must be completed, bonus points for the exam are awarded as follows: at least 30%: 1 bonus point, at least 50%: 2 points, at least 70%: 3 points, at least 90%: 4 points. The bonus points are taken into account in all the three exams of the course. If you get at least 3 bonus points your grade will increase by one.

At the beginning of each exercise session, a list will be given on which you can mark the problems you've solved and you are ready to write on the board. One person per problem is selected from the list to present his/her solution on the board. If you cannot attend the exercise, you can return your solutions to the problems on paper to box #300 before Tuesday's first exercise session. You find the box from the third floor of Institute of Signal Processing near E-wing. In order to get points, returned exercises should be solved correctly. Notice that you won't get your papers back and the correct answers you can get only by attending the exercises.

Exercise problems

To be posted soon.

Goal and contents:

The goal is to introduce basic methods and principles of pattern recognition. Basics of multivariate probability and statistics. Bayesian decision theory. Parameter estimation from training data. Non-parametric techniques for pattern classification. Algorithms for unsupervised classification.

Requirements:

Final examination and active participation in exercises.

Exams:

??.12.2004 (?-?)

About the exam (Fall 2003)

Literature:

Duda, Hart, Stork: Pattern Classification, 2nd edition, Wiley, 2001.

SGN-2506 Introduction to Pattern Recognition, 4 cr., Fall 2005

Lectures (28h):

Material

Exercises (14h):

Exercise groups in English:

Exercise groups in Finnish:

Requirements:

Exercise problems

Goal and contents:

Requirements:

Exams:

Literature:

Links:

In English:

In Finnish: