School of Science and Technology

Introduction to Machine Learning, 3 Credits

Örebro University is offering an introduction course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems.

Introduction to machine learning

After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University.

  • Basic concepts and algorithms for supervised and unsupervised learning.
  • Areas of application for machine learning algorithms for classification and prediction.
  • Methods of data preprocessing, such as normalisation, attribute extraction and selection, dimensionality reduction and balancing.
  • Practical recommendations for application of machine learning algorithms.
  • Evaluation and analysis of machine learning algorithm performance.

    Additional subjects: bias and variance balancing, reward-based learning, combination learning.

This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) with computer science/computer technology as the main field of study, or alternatively a higher education qualification of 180 credits earned at the first cycle (bachelor's level) in e.g. computer technology/computer science/systems science (which includes computer programming).

In addition, English B/English 6 is required.

The course is free of charge.

Apply to the course here.

The application is only available in Swedish at the moment. Please contact Johan Axelsson if you have any questions.

Coordinator of Smarter and professional education in AI

Johan Axelsson

Title: Contract Education Coordinator School/office: Communication and Collaboration

Profile page: Johan Axelsson


Phone: +46 19 303211

Room: E2204

Johan Axelsson

Course administrator

Jenny Tiberg

Title: Study and Research Administrator School/office: School of Science and Technology

Profile page: Jenny Tiberg


Phone: +46 19 303320

Room: T1109

Jenny Tiberg