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Karlstad University
Applied Machine Learning
The course provides an introduction to machine learning with a focus on applied deep machine learning. The first part of the course covers the basics of machine learning, including relevant theory, terminology, and principles, and introduces both classical and deep machine learning. Instruction is mainly in the for…
- Higher education
- Information unavailable
- 9 November 2026
- Karlstad
- Information unavailable
- 50 %
Overview
The course provides an introduction to machine learning with a focus on applied deep machine learning. The first part of the course covers the basics of machine learning, including relevant theory, terminology, and principles, and introduces both classical and deep machine learning. Instruction is mainly in the form of lectures and self-study of educational materials based on open-access sources. Some parts involve flipped classroom activities. The second part of the course focuses on applied deep machine learning through a number of practical exercises and combines lectures on necessary theory with laboratory sessions. The aim is to enable students to explore problem solution independently through using popular libraries and tools with open source code for deep machine learning. The course concludes with a laboratory task focused on deep machine learning which is presented orally in groups.
Admission scores
Entry requirements
60 ECTS credits completed including Data Structures and Algorithms, 7.5 ECTS credits, and Discrete Mathematics, 7.5 ECTS credits, or equivalent
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
- 2026-11-09
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.kau.dvgc27.55044.20262
- Last checked
- 2026-09-23T10:37:50.445791+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
Application and important dates
- Programme or course starts
- Programme or course ends
Salary and salary distribution
Common occupations after graduation
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Geographical background
Previous upper-secondary schools and programmes
Completion and outcomes
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.kau.dvgc27.55044.20262
- Offering identity in the source
- e.uoh.kau.dvgc27.55044.20262
- Education-form source code
- HS
- Education code in the source
- DVGC27
- Change time according to the source
- 2026-03-03T14:14:00
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.