MATE5439 Foundations of Machine Learning III (5 cr)

Cooperation network course

Network: Cross-institutional studies in advanced courses in mathematics and statistics

This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:

  • Bachelor's Degree Programme in Mathematics
  • Master's Degree Programme in Mathematics
  • Bachelor's Degree Programme in Mathematics (Subject Teacher)
  • Master's Degree Programme in Mathematics (Subject Teacher)
  • Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
  • Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
  • Doctoral Programme in Mathematics and Statistics
  • Doctoral Programme in Mathematics and Science (Specialication in Mathematics)

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Description

The course builds on the Foundations of Machine Learning I and II courses and has them (or their content) as prerequisites. It focuses on combining multiple learners, on design and analysis of machine learning experiments, on kernel machines and graphical models. The courses combines the theory of machine learning with hands-on projects allowing the students to experiment with training various machine learning models on realistic datasets.

Learning outcomes

After passing this course, the students will be able to describe the mathematical and algorithmics foundations of the machine learning approaches discussed in this course. They will also be able to implement machine learning models using the techniques discussed in the course, to train them, and use them for predictions.

Additional information

Lectures are not given every year but it's also possible to take the course independently. Academic year 2026-2026 available only for independent study.

Description of prerequisites

MATE5427 Foundations of Machine Learning I MATE5428 Foundations of Machine Learning II