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An introduction to machine learning for industrial measurements

The webinar introduces the basic terminology associated with machine learning and gives a high-level overview of how metrology principles of uncertainty, calibration and traceability could be applied to ensure confidence in machine learning outputs. Case studies in industrial settings are also presented. The webinar is free to attend but registration is esential.

The webinar will provide an introduction to machine learning (ML), a branch of artificial intelligence that is based on the idea that computers can learn patterns, rules or structure from data without being given specific instructions about the task they are performing. ML models are often “black box” in the sense that the model inputs and outputs are known but the inner workings of the model are not understood. It is therefore challenging to assess confidence in the outputs of ML models – a crucial requirement for the digital transformation of manufacturing.

We introduce the basic terminology associated with ML and give a high-level overview of how metrology principles of uncertainty, calibration and traceability could be applied to ensure confidence in ML outputs. Case studies of ML in industrial settings are also presented. The webinar comprises recorded video presentations to be made available on the project website, together with time for questions and opportunity for discussion about the presentations.

It is suitable for individuals who would like to learn about the basics of machine learning and how it could be applied to industrial applications.

This webinar is an output of the European Metrology Research Programme (EMPIR) project, Metrology for the Factory of the Future (Met4FoF). The project aims to establish a metrological framework to support the complete lifecycle of measured data in industrial sensor networks to ensure transparency, comparability and sustainable quality of measured data, processing methods and measurement results.

3rd March

09:00 to 11:30 GMT


Webinar

Online

Register here