Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is
extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt
both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a
disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and
dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine
learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we
embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and
graph-based machine learning for the networking layer
