Teaching Lab

Machine Learning for Mobile and Pervasive Systems P

Applying machine learning algorithms into real-life data in group project work.
Students watching posters about machine learning.
Final poster presentation. / photo: Stephan Sigg, Aalto University


The idea of the course is that the students apply the learned content in the course (machine learning algorithms, good practices how to prepare and collect data, presentation of results) in projects on real and relevant data. For this, all lectures are concentrated in the first period, so that the theory is conveyed quickly and can then be applied by the students.

Master's level course.


Teaching methods

There is not a final examination but replaced this with assignments throughout the course. The final grade is composed from the score in the assignments (report, tutorial, poster presentation).

  • Good basic knowledge by traditional lectures covering standard machine learning methods in-depth (concepts and mathematical details),
  • Broad overview,offering invited lectures and tutorials on various topics that exceed what was covered in the lecture also available online,
  • In-depth practical experience in one method) requiring the students to solve an actual machine learning problem in groups and providing hands-on practical training on python ML-tools.
  • Students present a poster comprising their results and the project conducted. In addition, the students have written a report on the topic.



Aalto School of Electrical Engineering, Department of Communications and Networking.


Course code

ELEC-E7260 Machine Learning for Mobile and Pervasive systems P


Links & Materials



Contact person

Stephan Sigg

Pilot Funding for work-life relevant education

We are here to help.

Laura Sivula

Laura Sivula

School Services, BIZ
Silva Saulio

Silva Saulio

Project coordinator
School Services, BIZ
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