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Mälardalen University
Deep Learning and Neural Networks
<p>Deep learning is a recent topic within machine learning and artificial intelligence. In deep learning, we have models of large and complex artificial neural networks that imitate how the human brain processes information in presence of large amounts of data. Today deep learning is one of the most exciting and inn…
- Higher education
- Information unavailable
- 10 November 2025
- Information unavailable
- Information unavailable
- 50 %
Overview
<p>Deep learning is a recent topic within machine learning and artificial intelligence. In deep learning, we have models of large and complex artificial neural networks that imitate how the human brain processes information in presence of large amounts of data. Today deep learning is one of the most exciting and innovative parts of machine learning. When the amount of data increases, and the problems become more complex, we are facing new challenges; and this is when we need deep learning. The subject is under strong development and will be important for advanced products and services today and in the future. </p>
Admission scores
Entry requirements
Completed courses of 60 credits in computer science, of which includes Programming 7,5 credits, Data Structures, Algorithms and Program Development 7,5 credits and Artificial Intelligence 1 7,5 credits. In addition basic vector algebra 7,5 credits and basic calculus 7,5 credits are needed.
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
- 2025-11-10
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.mdu.dva307.24079.20252
- Last checked
- 2026-09-23T10:38:46.21657+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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- Programme or course starts
- Programme or course ends
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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.mdu.dva307.24079.20252
- Offering identity in the source
- e.uoh.mdu.dva307.24079.20252
- Education-form source code
- HS
- Education code in the source
- DVA307
- Change time according to the source
- 2025-03-14T10:56:08
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.