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Stockholm University
Machine Learning for Physicists and Astronomers
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…
- Higher education
- Information unavailable
- 19 January 2026
- Information unavailable
- Information unavailable
- 50 %
Overview
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.
Admission scores
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.
The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.
Source, measure and data quality
- Source
- Skolverket Susa-navet
- Period
- 2026-01-19
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.su.fk7068.47085.20261
- Last checked
- 2026-09-23T10:39:10.072499+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
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About the provider
Sources and data quality
Education facts for the selected offering come from Skolverket Susa-navet.
Retrieved . Published . Times are shown in Swedish local time.
Source identity and publication version
- Publication version
- 8e217193-f5fa-4778-b085-a4521fd03e8d
- Education identity in the source
- i.uoh.su.fk7068.47085.20261
- Offering identity in the source
- e.uoh.su.fk7068.47085.20261
- Education-form source code
- HS
- Education code in the source
- FK7068
- Change time according to the source
- 2025-08-19T11:19:25
The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.