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Halmstad University
Machine Learning for Predictive Maintenance
The courses is for professionals and part of the programme MAISTR (hh.se/maistr) where participants can study the entire programme or individual courses. The course is part of the course track machine learning and is held online in English. <br> The course covers the following topics: - Definition and terminology…
- Higher education
- Information unavailable
- 29 March 2027
- Ortsoberoende
- Information unavailable
- 33 %
Overview
The courses is for professionals and part of the programme MAISTR (hh.se/maistr) where participants can study the entire programme or individual courses. The course is part of the course track machine learning and is held online in English. <br> The course covers the following topics: - Definition and terminology of relevant concepts in PdM - PdM task formulation, i.e. determine approaches and learning settings for given problems - Data engineering for time series data, including transformation, anomalous value detection, missing value imputation, outlier removal, etc. - PdM evaluation metric, given concrete applications and its formulation - Benchmarking the performance with traditional approaches - Transfer learning for fault detection and remaining useful life prediction - Survival analysis for PdM focusing on the evaluation metric and specialized cost function learning - Interpretability of the PdM machine learning models, and hybrid approaches
Admission scores
Entry requirements
Degree of Bachelor of Science in Engineering, including an independent project 15 credits or Degree of Bachelor of Science including an independent project 15 credits or the equivalent of 180 Swedish credit points or 180 ECTS credits at an accredited university. 7.5 credits machine learning, 7.5 credits data recovery and 7.5 credits programming. English 6 or English level 2. Exemption of the requirement in Swedish is granted for those with foreign grades.
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
- 2027-03-29
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.hh.dt8059.23557.20271
- Last checked
- 2026-09-23T10:37:30.869721+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
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Common occupations after graduation
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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.hh.dt8059.23557.20271
- Offering identity in the source
- e.uoh.hh.dt8059.23557.20271
- Education-form source code
- HS
- Education code in the source
- DT8059
- Change time according to the source
- 2026-08-28T12:40:28
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.