Since its commissioning in 2010, SDO has captured one high resolution image every second. A large community effort resulted in the implementation of a series of algorithms for the autonomous detection and tracking of solar events, with outputs available within the Heliophysical Event Knowledgebase (HEK). Machine learning and deep learning have the ability to further leverage on these data, algorithms and database, and to tackle open complex problems. This session will discuss mathematical, statistical, computational, and machine learning techniques for information processing of SDO and solar data, with emphasis on: autonomous prediction of solar events from heterogenous data (EUV imagers, magnetogram, spectrometer), supervised and unsupervised detection and tracking of solar events and solar features at large and small scales, dimensionality reduction, enhancing SDO images, enriching and mining the HEK e.g. in view of spatio-temporal pattern discovery. Of special interest are techniques that allow for a progress towards reconciling theory with observations.