Uusi opinto-opas (sisältäen myös opetusohjelmat) lukuvuodelle 2018-2019 sijaitsee osoitteessa https://opas.peppi.utu.fi . Tältä sivustolta löytyvät enää vanhat opinto-oppaat ja opetusohjelmat.

The new study guide (incl. teaching schedules) for academic year 2018-2019 can be found at https://studyguide.utu.fi. This site contains only previous years' guides.

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Archived Curricula Guide 2013–2014
Curricula Guide is archieved. Please refer to current Curricula Guides
TKO_3102 Machine Learning and Neural Networks 5 ECTS
Organised by
Computer Science
Person in charge
Jukka Heikkonen

Learning outcomes

The course gives an overview of many machine learning and neural networks methods which can be used to build models and systems based on observed data. After the course students should understand the main principles of machine learning and neural networks methods and steps needed for applying them in real applications.


This course covers the main theories, techniques, and algorithms in machine learning and neural networks, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as universal approximators, graphical models and modern Bayesian approach. Both main unsupervised and supervised learning techniques are considered with emphasize on how, why and when they work.

Teaching methods

Teaching method Contact Online
Lectures 28 h 0 h

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Doctoral Students
  • Exchange Students
Written exam
  • In English
Project / practical work
  • In English
Written exam and Project / practical work

Written exam and project work.


Numeric 0-5.

Study materials

Marsland S., Machine Learning: An Algorithmic Perspective,Chapman & Hall/CRC 2009

Bishop C.M., Pattern Recognition and Machine Learning, Springer 2006

Bishop C.M., Neural Networks for Pattern Recognition, Oxford University Press 1995.

Belongs to following study modules

Department of Future Technologies
Department of Future Technologies
Department of Future Technologies
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Department of Future Technologies
DP in Computer Science
DP in Electr. and Communication Technology
Finnish Study Modules