NEWS

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. |

Prerequisites:

SGN-2506 Introduction to Pattern Recognition (Lectures) |

SGN-2500 Johdatus hahmontunnistukseen (Luennot) |

Requirements:

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. |

Tasks:

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 |

Literature:

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. |