University of Skövde

Undervisningen ges på svenska. Viss undervisning på engelska kan förekomma. Att etablera en datadriven kultur i en grupp eller i ett team är inte lätt. En datadriven kultur innebär att gruppens medlemmar ofta använder olika typer av verktyg för att analysera data och diskuterar sina insikter, innan de fattar beslut. I den här kursen får du lära dig hur mognadsgraden av datadriven kultur kan etableras och mätas i en befintlig grupp. Lär dig förstå vad datadriven kultur är och hur mognadsgraden kan analyserasKursen består av två delar: en mognadsanalys och en åtgärdsplan.  Först kommer du att få genomföra en mognadsanalys av datadriven kultur i en befintlig grupp i din egen verksamhet. Mognadsanalysen är baserad på tidigare forskning inom Analytics och grupputveckling (ex Wheelans modell).  Utifrån genomförd mognadsanalys kommer du att få ta fram en åtgärdsplan som är förankrad i aktuell forskningslitteratur.  Vem är kursen för? Kursen riktar sig till dig som är yrkesverksam och intresserad av att lära dig mer om hur en datadriven kultur kan etableras och analyseras i en grupp eller ett team.  Efter avslutad kurs kan du: genomföra en mognadsanalys av datadriven kultur i en grupp, och ta fram en åtgärdsplan som är förankrad i aktuell forskningslitteratur för hur en grupp skapar förutsättningar för en ökad mognadsgrad.  Kursformat Kursen är i sin utbildningsform och omfattning tänkt att kombineras med arbete. Det innebär bland annat att: Kursen ges online och genomförs genom självstudier samt enstaka frivilliga schemalagda tillfällen där du får möjlighet att träffa övriga kursdeltagare och kursens lärare. (Dessa träffar sker på distans via Zoom) Det är en kort kurs (3 Högskolepoäng) med en studietakt på 10 %. Undervisningen bedrivs främst på svenska, moment på engelska kan förekomma.  Behörighet Om du inte uppfyller de formella behörighetskraven kan du få din behörighet bedömd på reell kompetens, kunskap och kompetens som du har fått på annat sätt, såsom arbetslivserfarenhet, övriga studier med mera. Läs mer under his.se/sokwiser.  Utvecklad inom WISER Kursen är utvecklad inom projektet WISER. Vi erbjuder skräddarsydda kurser för digital transformation och riktar sig till dig som är yrkesverksam. Projektet samfinansieras av KK-stiftelsen inom ramen för Expertkompetens. För mer information besök: his.se/wiser.    

The course is taught in English   Quality control and defect detection are crucial in most industrial production processes. With modern technologies in Artificial Intelligence (AI), these processes can be automated and enhanced through image-based quality inspections, known as vision systems. This course provides you with a clear understanding of how neural networks work and how they can be used to create effective AI systems for quality control in industry. In the course, you will learn how neural networks function, particularly for image processing, and how different types of networks can be used for various image-based tasks. The course also addresses challenges that may arise with data when training neural networks. Through practical exercises, you will develop a simple AI-based quality control system using appropriate software tools. Who is the course for?This course is aimed at professionals working in the industrial sector who want to learn more about how AI and neural networks can be used to improve quality control. It is particularly useful for engineers, technical experts, and IT specialists working with automation and production efficiency. After completing the course, you will be able to: Describe and explain how neural networks operate for image-based tasks, Discuss different types of networks and how they can be applied to various image-based tasks, Understand data-related challenges that may occur when training neural networks, Implement AI systems for quality control using standard software tools. Course formatThe course is designed to be combined with professional work, meaning: The course is delivered online with pre-recorded lectures, It is a short course (3 ECTS credits) with a study pace of 20% (approximately 8 hours per week over 10 weeks). The instruction is primarily conducted in English. Entry RequirementsIf you do not meet the formal entry requirements, you may have your eligibility assessed based on prior learning, including skills and knowledge acquired through work experience, other studies, and more. Read more at his.se/sokwiser. Developed within WISERThe course is developed within the WISER project. We offer tailored courses for digital transformation aimed at professionals. The project is co-financed by the Knowledge Foundation (KK-stiftelsen) within the framework of Expertkompetens. For more information, visit: his.se/wiser.

The course is taught in English   Modern Artificial Intelligence (AI) is based on the idea that models can be constructed through training using data. Different design choices regarding both the type of model and the training procedure can be crucial for the final result. This course covers the fundamentals of modeling from the perspective of various uncertainties that may arise. The course is based on a theory that often serves as a foundation for uncertainty modeling within the fields of artificial intelligence, machine learning, and data science. The goal in these fields is often to extract knowledge and use models for decision-making or prediction. The course addresses key concepts and tools for probabilistic modeling, as well as programming techniques to efficiently handle data and build models. Specifically, so-called probabilistic programming will play a central role as a modeling tool throughout the course. Who is the course for?The course is aimed at professionals in industry who want a deeper understanding of uncertainties that can arise during modeling in the AI field. After completing the course, you will be able to: demonstrate the use of tools for programming with data, show understanding of fundamental concepts related to uncertainty and probabilistic modeling within AI/data analysis, demonstrate the use of tools for probabilistic modeling, analyze, assess, and show understanding of training results in terms of uncertainty, and analyze a model’s performance by exploring uncertainty using visualization. Course formatThe course is designed to be combined with work, meaning: The course is delivered online with pre-recorded lectures,It is a short course (3 ECTS credits) with a study pace of 20% (approximately 8 hours per week over 10 weeks).The language of instruction can, depending on the course occasion, be either Swedish or English. If the course is taught in Swedish, some parts may still be conducted in English. Entry RequirementsIf you do not meet the formal entry requirements, you may have your eligibility assessed based on prior learning, knowledge and competencies you have acquired in other ways, such as work experience or other studies. Read more at his.se/sokwiser. Developed within WISERThis course has been developed as part of the WISER project. We offer tailored courses for digital transformation aimed at professionals. The project is co-financed by the Knowledge Foundation (KK-stiftelsen) within the framework of Expertkompetens. For more information, visit: his.se/wiser.