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
2012 spring 12.03-27.04. 4-4 English Jukka-Pekka Kauppi

Ilmoittautuminen tälle kurssille alkaa tiistaina 21.2. klo 9.00.

Registration for this course starts on Tuesday 21st of February at 9.00.


Relevant information concerning the project works is available in this web page and exercise handouts, but short description how to complete the assignments will be given in the beginning of the exercise session at 23th of March.

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. There will be altogether three assignments with the following topics:

  1. Principal component analysis (PCA) and factor analysis. Handed out March 23 (Fri), deadline April 6 (Fri).
  2. Independent component analysis (ICA). Handed out April 7 (Sat), deadline April 25 (Wed).
  3. Clustering and projection methods. Handed out May 2 (Wed), deadline May 16 (Wed).


Completing the course

For every computer assignment, you will need to:

  • Implement core methods from the course Unsupervised Machine Learning using Matlab or R.
  • Write a report where you present and discuss your solution.

The grade will be based on the written reports, so make them clear and enjoyable to read!

Reports should be sent to Jukka-Pekka Kauppi (jukka-pekka.kauppi{at} by the deadlines shown above (by midnight). If you have questions concerning the assignments, send e-mail to Jukka-Pekka Kauppi or come and discuss to room A313 (Exactum building) Wednesdays 14-15 or Thursdays 16-17.

Literature and material

matlab/R reference