Soubelet, F. Apsimon, Ö. Persson, T.H.B. Tomás García, R. Welsch, C.P. JACoW Publishing Geneva, Switzerland 2673-5490 978-3-95450-227-1 10.18429/JACoW-IPAC2022-WEPOPT008 English 1842-1845 Contribution to a conference proceedings 2022 2022-07 https://doi.org/10.18429/JACoW-IPAC2022-WEPOPT008 https://jacow.org/ipac2022/papers/wepopt008.pdf Local interaction region (IR) linear coupling in the LHC has been shown to have a negative impact on beam size and luminosity, making its accurate correction for Run 3 and beyond a necessity. In view of determining corrections, supervised machine learning has been applied to the detection of linear coupling sources, showing promising results in simulations. An evaluation of different applied models is given, followed by the presentation of further possible application concepts for linear coupling corrections using machine learning.