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Machine learning in the real world (Signe Riemer-Sørensen, SINTEF)

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Machine learning algorithms are flexible and powerful, but the data requirements are high and rarely met by the available data. Real world data is often medium sized (relative to problem side), noisy and full of missing values. At the same time, in order to deploy machine learning in industrial settings, they must be robust, explainable and have quantified uncertainties. I will show practical examples of these challenges from our recent projects and some case-by-case solutions, but also highlight remaining issues.

Tuesday, October 8, 2019 - 11:00 to 12:00
Inria Lille - Nord Europe, Salle plénière
Signe Riemer-Sørensen