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Uppsala University
Introduction to Machine Learning
This is a practical introduction to machine learning: its terminology, an overview of basic supervised and unsupervised methods (for example, regression, classification trees, an introduction to neural networks and deep learning, and clustering), use of established tools for machine learning and practical aspects su…
- Higher education
- Information unavailable
- 18 January 2027
- Uppsala
- Information unavailable
- 33 %
Overview
This is a practical introduction to machine learning: its terminology, an overview of basic supervised and unsupervised methods (for example, regression, classification trees, an introduction to neural networks and deep learning, and clustering), use of established tools for machine learning and practical aspects such as dimensionality reduction and cross-validation.
Admission scores
Entry requirements
60 credits of which 15 credits in mathematics and 15 credits in computer science. Probability and Statistics or Probability and Statistics DV. Algebra and Geometry or Linear Algebra and Geometry I. Participation in a second course in programming.
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
- 2027-01-18
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.uu.1dl034.61027.20271
- Last checked
- 2026-09-23T10:39:41.205927+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
Programme content
Study structure
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- Programme or course starts
- Programme or course ends
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Previous upper-secondary schools and programmes
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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.uu.1dl034.61027.20271
- Offering identity in the source
- e.uoh.uu.1dl034.61027.20271
- Education-form source code
- HS
- Education code in the source
- 1DL034
- Change time according to the source
- 2026-09-10T10:12:48
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.