Machine Learning for Physicists and Astronomers

Education facts

Code: FK7068

Machine learning is one of the fastest growing and most dynamic areas of modern physics research and data application. This course gives an introduction to the core concepts, theory and tools of machine learning as required by physicists addressing practical data analysis tasks. Use cases and limitations of machine learning algorithms will be discussed. The implementation and use of machine learning in practical applications will be exemplified, and realistic scenarios will be studied in applications relevant to physics research and astronomy.

Entry requirements

Admission to the course requires knowledge equivalent to passed courses (excluding introductory courses) of 45 credits in mathematics and 60 credits in physics, where the courses Programming, Numerical Methods and Statistics for Physicists, 15 credits (FK4026), and Mathematics II - Linear Algebra, 7.5 credits (MM5012) should be included. Additionally, requires knowledge equivalent to upper secondary school English B/English 6.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Machine Learning for Physicists and Astronomers

  Stockholm University

  Start date: 19 January 2026

  End date: 22 March 2026

  Pace of study: 50 %