Projects in Unsupervised Machine Learning

Algorithms and machine learning
Advanced studies
Practical implementation of methods taught in the course Unsupervised Machine Learning, in a number of short computer projects. The projects are done in parallel to the course. The project work can be done in addition to or as an alternative to taking the course exam.


Year Semester Date Period Language In charge
2011 spring 14.03-29.04. 4-4 English Michael Gutmann

Ilmoittautuminen tälle kurssille alkaa tiistaina 22.2. klo 9.00.

Registration for this course starts on Tuesday 22nd of February at 9.00.

Information for international students

Everything will be in English.


You can give feedback by following this link (advanced studies ->projects in Unsupervised Machine Learning). Thank you for helping to improve the course!

The projects will be in the form of computer assignments where you will solve some practical problems using methods that are taught in the course Unsupervised Machine Learning. To get an idea of what kind of problems you will solve, have a look at the computer assignments from last year's course.

Part of the exercise classes of the course Unsupervised Machine Learning will be used to discuss the computer assignments.

We will have three assignments, with the following topics:

  1. principal component analysis (PCA), dimension reduction and factor analysis
    Handed out: Fr April 1.
  2. independent component analysis (ICA)
    Handed out: Thu April 14
  3. clustering and projection methods
    Handed out: Fr April 29

You will have two weeks time for each assignment which means that you will need to hand in the last assignment two weeks after the lecture has finished (see schedule above).

Completing the course

For every computer assignment, you will need to:

The grade will be based on the reports, so make them nice and enjoyable to read.

Literature and material

matlab/R reference


  1. First assignment: handout, data for third exercise (1.2MB), matlab script to visualize images, R script to visualize images (taken from intro to machine learning course).
    Due :  So April 17, midnight.
  2. Second assignment: handout, data for third exercise (5.2MB), Typos, comments and corrections
    Due :  Mo May 2, noon
  3. Third assignment: handout, data for second exercise (12.5MB) , hints
    Due :  So May 15, midnight