582638 Unsupervised machine learning

Lecture course in English, 4-6 cu (ECTS), 2009-2010


Lectures: Aapo Hyvärinen
Exercices and computer projects: Doris Entner and Michael Gutmann


In the 4th period, starts 16/03/2010, ends 30/04/2009. Sessions are Tuesdays, Thursdays and Fridays, 14:15-15:45 at lecture room C222.

There are typically two lecture sessions each week (Tue, Thu) and one exercice session (Fri), but there are some expections. Here's the detailed schedule:

Tue 16 Mar Lecture * Thu 18 Mar Lecture * Fri 19 Mar Exercices
Tue 23 Mar Lecture * Thu 25 Mar Lecture * Fri 26 Mar Exercices
Tue 30 Mar No teaching * Thu 1 Apr Easter break * Fri 2 Apr Easter break
Tue 6 Apr Easter break * Thu 8 Apr Exerc (1h) + Lect (1h) * Fri 9 Apr Lecture
Tue 13 Apr Lecture * Thu 15 Apr Lecture * Fri 16 Apr Exercices
Tue 20 Apr Lecture * Thu 22 Apr Lecture * Fri 23 Apr Exercices
Tue 27 Apr Lecture * Thu 29 Apr Lecture * Fri 30 Apr Exercices


Please register using the ILMO system. If you don't have permissions to this system, register at the first lecture.

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.

Mathematical exercices (Friday sessions) are not obligatory but they will give extra points to be added to your points in the exam and/or computer assignments, up to a maximum of 25% more points.

If you do both exam and computer assignments, the grade will be the average of the two (with possible exercice points added as explained above).



Course material

The complete lecture notes are here. Just to keep search engines away, you need the login uml and password uml. There is no book for the course.

Exercices are here.

Aapo Hyvärinen, Dec 2009. Last update 19 Apr 2010.