582638 Unsupervised machine learning

Lecture course in English, 4-6 cu (ECTS), 2008-2009

Home page for next year's course (2009-2010)


Lectures: Aapo Hyvärinen
Exercices and computer projects: Urs Köster and Michael Gutmann


In the 4th period, starts 11/03/2009, ends 24/04/2009. Lectures are on Wednesdays and Fridays, 14:15-15:45 at lecture room C222. Exercice sessions are on Tuesdays, 14:15-15:45 at room BK106.

Note also Easter break: no lectures or exercices on 10th, 14th, 15th April.


Please register using the ILMO system.

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Target audience

Master's students in statistics (incl. EuroBayes), computer science (specialization in algorithms & machine learning, intelligent systems, or bioinformatics), or applied mathematics (specialization e.g. in stochastics)


Unsupervised learning is one of the main streams of machine learning, and closely related to exploratory data analysis and data mining. This course describes some of the main methods in unsupervised learning.

In recent years, machine learning has become heavily dependent on statistical theory which is why this course is somewhere on the borderline between statistics and computer science. Emphasis is put both on the statistical formulation of the methods as well as on their computational implementation. The goal is not only to introduce the methods on a theoretical level but also to show how they can be implemented in scientific computing environments such as Matlab or R. Computer projects are an important part of the course.

How to obtain the credits

There are two ways of getting credits for this course:

If you do one of these, you get 4 cu. If you do both of them, you get 6 cu. You are strongly encouraged to do both of them.

You also have the option of handing in the mathematical exercices given in the exercice sessions. This will give you extra points worth, at the maximum, 20% of the total points. Details



Course material

There is no book for the course. Handouts, typically chapters of books, will be provided, and together with some lecture slides, these will contain the material of the lectures. This material, together with the exercices, will be made available here (material will be added after each lecture). You will need a login name and password which are given during the lectures (or you can email the assistants)

Aapo Hyvärinen, Feb-April 2009. Last update 26 Apr 2009.