The session welcomes all papers that are related to learning approaches to electromagnetic inverse scattering problems or imaging problems. The session aims at presenting frontier research in the theories, computations, and applications of machine learning for solving electromagnetic inverse scattering problems or imaging problems. All types of learning algorithms are welcomed: supervised and semi-supervised learning, unsupervised learning, and reinforcement learning. Applications include but not limited to microwave imaging, through-wall-imaging, non-line-of-sight imaging, subsurface detection, electric impedance or capacitance tomography, biomedical imaging, near-field optical imaging, non-destructive testing/evaluation, remote sensing, radar imaging, phaseless imaging, and millimetre-wave short-range imaging.