This offering is not in the current catalogue. The information is retained from an earlier publication. Check the provider's current offering.
University West
Industrial digitalization - Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools used in a wide variety of areas such as finance, healthcare, e-commerce, industry, entertainment and many others to automate numerous tasks. For example, in healthcare an ML algorithm can be used to predict a diagnosis based on the patient’s d…
- Higher education
- Information unavailable
- 26 January 2026
- Information unavailable
- Information unavailable
- 30 %
Overview
Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools used in a wide variety of areas such as finance, healthcare, e-commerce, industry, entertainment and many others to automate numerous tasks. For example, in healthcare an ML algorithm can be used to predict a diagnosis based on the patient’s data. An autonomous vehicle can employ an AI algorithm to decide whether to apply emergency breaking. Fraud and money laundering can also be successfully identified by AI and ML technology when used by a financial institution. Successful deployment of this technology requires understanding of application areas of different AI and ML algorithms and their performance characteristics. This course introduces the most common concepts ML and AI. Attention is given to proper applicability of different algorithms to diverse types of data. Importance of the quality of the data is emphasized as one of the keys to successful data analysis. The examination of the course is based on field study where the students apply the knowledge in their own area of expertise. The target group for the course is senior and medium level management as well as engineering and technical personnel unfamiliar with AI and ML technology. This course will be delivered on distance. The outline of the course is one online introduction session, pre-recorded lectures, online exercises and examination. <br> [LÄS MER OM KURSERBJUDANDET](<https://admin.hv.se/globalassets/bifogade-filer/industrins-digitalisering---introduktion-till-artificiell-intelligens-och-maskininlarning.pdf/>) <br> NOTE suitable prior knowledge for this course is the basic course in programming with Python, PIA600, Programming for Industrial Applications 3 HE credits. **Industry and academy**<br> This course has been developed in collaboration with industry partners within the framework of one of our educational projects. The project aims to provide courses at the advanced level to a mix of professionals and students to strengthen the Swedish industry's competitiveness.<br> We apply validation of work experience to see if you have knowledge that corresponds to the eligibility requirements for the course. Do not forget to attach a description if you are applying based on work experience, a form is available on our website [www.hv.se/produktionskurser](<https://www.hv.se/produktionskurser>).
Admission scores
Entry requirements
Passed courses of 90 HE credits within the field of technology with at least 15 HE credits on level 61-90 HE 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
- 2026-01-26
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.hv.idm600.26002.20261
- 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
Students
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.hv.idm600.26002.20261
- Offering identity in the source
- e.uoh.hv.idm600.26002.20261
- Education-form source code
- HS
- Education code in the source
- IDM600
- Change time according to the source
- 2025-09-03T09:25:32
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.