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University of Gothenburg
Advanced machine learning with neural networks
This course introduces students to recent developments and state-of-the-art methods in machine learning using artificial neural networks. This advanced course builds on Machine learning with neural networks (FFR135) and provides an in-depth analysis of many of the concepts and algorithms that were briefly introduced…
- Higher education
- Information unavailable
- 23 March 2026
- Information unavailable
- Information unavailable
- 50 %
Overview
This course introduces students to recent developments and state-of-the-art methods in machine learning using artificial neural networks. This advanced course builds on Machine learning with neural networks (FFR135) and provides an in-depth analysis of many of the concepts and algorithms that were briefly introduced in that course, with particular emphasis on applications in the natural and engineering sciences. The goal is to become familiar with several advanced machine-learning methods, and to code them efficiently in Python using current neural-network packages. An essential part of the course are projects in deep learning and reinforcement learning.
Admission scores
Entry requirements
A Bachelor's degree in physics, mathematics, computer science, or similar including 30 credits of mathematics.Applicants must prove their knowledge of English: English 6/English B from Swedish Upper Secondary School or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.
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
- 2026-03-23
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.gu.fym360.11053.20261
- Last checked
- 2026-09-23T10:36:47.551594+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.gu.fym360.11053.20261
- Offering identity in the source
- e.uoh.gu.fym360.11053.20261
- Education-form source code
- HS
- Education code in the source
- FYM360
- Change time according to the source
- 2026-01-27T14:29:43
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.