8002303 Pattern Recognition, Spring 2006, 5 credits


6.04. There will be no lecture at that day at 12-14. This lecture is shifted to 20.04 at 14-16. Respectively, the group participating the exercises on thursday at 14-16 will have no exercise class on 20.04. This exercise is shifted to 6.04: at 12-14 in the room TC407 (instead of the lecture). The exercise on 6.04. at 14-16 will be given according to the schedule.
11.04. The lecture at that day is shifted from 12-14 to 14-16. The exercise group participating the exercises on tuesdays at 14-16 will have their exercise at 12-14 at lecture time in the room TC 415.
12.04.-18.04. Easter holidays. No lectures and exercises at that days.
NOTICE: The exam will be graded with numeric assesment (1-5).
  Lecture notes available for copying in the pigeon hole in Tietotalo, 4th floor.

Lectures (24 h):

Lectures are given by Prof. Ulla Ruotsalainen and Prof. Ari Visa.
First lecture: 28.03.2006
Time and Place: Tuesdays 12-14, TB 219
Thursdays 12-14, TB223

Exercises (12h):

Time and place:
1 group Tuesdays 14-16, room TC415
2 group Tuesdays 16-18, room TC415
3 group Thursdays 14-16, room TC415
You have to choose one exercise group at time most suitable to you and participate it.
First exercise: 28.03 and 30.03.


SGN-2506 Introduction to Pattern Recognition (Lectures)
SGN-2500 Johdatus hahmontunnistukseen (Luennot)


Written exam, set of exercises and two small homeworks. In order to pass the course the exercises and the homeworks should be completed. After each session you have to write a short report (2-3 pages long) according to the following template .
The assistants for the exercises are: Evgeny Krestyannikov and Carol Rus. Exercises 1-3 will be given by Evgeny Krestyannikov, exercises 4-6 by Carol Rus. Please, return the reports by email to the respective assistants within one week after each exercise session.


Exercise 1: Bayes classifier. Matlab files
Exercise 2: Linear classifiers (perceptron,LMS,MSE). Matlab files
Homework 1: Multicategory classifiers. Matlab files
Exercise 3: Stochastic classifiers (Boltzmann machine). Matlab files
Exercise 4: Tree classifiers. Matlab files (updated)
Homework 2 (Due to 12.05.2006): Algorithm-independent machine learning. Matlab files
Exercise 5: Projections and feature extraction. Component and discriminant analysis. Matlab files
Exercise 6: Unsupervised learning and clustering (k-means,hierarchical, probabilistic). Independent component analysis. Matlab files

Accepted reports:

List of accepted reports can be found here


R.Duda, P.Hart and D.Stork: Pattern Classification, 2nd edition, Wiley-Interscience, New-York,2001(Powerpoint lecture slides)

The following chapters will be be covered during the course:
Chapter 2: Bayesian decision theory and belief networks
Chapter 3: Bayesian parameter estimation, sufficient statistic, Expectation-Maximization method, Hidden Markov Models
Chapter 5: The whole chapter
Chapter 7: The whole chapter
Chapter 8: The whole chapter
Chapter 9: The whole chapter
Chapter 10: Repetition of mixture densities, clustering, component analyses(PCA,ICA,NLCA,Factor analysis)
In addition: Fuzzy methods from all chapters.