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Linköping University
Machine Learning
<p>This course is aimed at those who want to gain a broad knowledge of different types of machine learning and an understanding of how different machine learning methods work, either if you are working as an engineer and want further education or want to complement on-going higher education with a course in machine…
- Higher education
- Information unavailable
- 31 August 2026
- Information unavailable
- Information unavailable
- 33 %
Overview
<p>This course is aimed at those who want to gain a broad knowledge of different types of machine learning and an understanding of how different machine learning methods work, either if you are working as an engineer and want further education or want to complement on-going higher education with a course in machine learning. The course takes off with an introduction to the field of machine learning, discussing the three different basic principles for machine learning – Supervised, Unsupervised, and Reinforcement Learning. Then we take a closer look at some specific methods within each of these areas. Within Supervised Learning, we first look at linear classifiers followed by Neural Networks, including Deep Learning and Convolutional Neural Networks. Within the category Unsupervised Learning, we cover data analysis methods such as Principal Component Analysis and Clustering. Finally, we look at Q-learning as an example of Reinforcement Learning. </p><p>The course contains 4 programming exercises that should be reported in written form and comprise the examination of the course. The course is given completely on distance with scheduled occasions for seminars and supervision. </p><p>As the course is given in English, this is a prerequisite. If your high school qualifications are not already on your pages on antagning.se, you need to upload your high school diploma, or equivalent, on antagning.se in connection with your application.</p>
Admission scores
Entry requirements
90 ECTS credtis passed in an engeineering subject and/or natural sciences of which at least 5 ECTS credits Multivariate calculus, 5 ECTS credits Linear Algebra, 5 ECTS credits Statistics, 5 ECTS credits Python programming or equivalent work experience of at least 1 year on a half time basis where Python programming is part of the work tasks English corresponding to the level of English in Swedish upper secondary education (Engelska 6 or Engelska nivå 2) Exemption from Swedish
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
- 2026-08-31
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.liu.ete391.1t033.20262
- Last checked
- 2026-09-23T10:38:02.783888+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
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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.liu.ete391.1t033.20262
- Offering identity in the source
- e.uoh.liu.ete391.1t033.20262
- Education-form source code
- HS
- Education code in the source
- ETE391
- Change time according to the source
- 2026-02-03T16:38:23
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.