  Machine Learning (ML) is the branch of computer science that studies computer
algorithms that can learn from data. It is mainly divided into supervised
learning, where the computer is presented with examples of entries, and the
goal is to learn a general rule that maps inputs to outputs, and unsupervised
learning, where no label is provided to the learning algorithm, leaving it
alone to find structures. Deep learning is a branch of machine learning based
on numerous layers of artificial neural networks, which are computing systems
inspired by the biological neural networks that constitute animal brains. In
asteroid dynamics, machine learning methods have been recently used to identify
members of asteroid families, and to identify resonant arguments images of
asteroids in three-body resonances, among other applications. Here, we will
conduct a review of available literature in the field, and classify it in terms
of metrics recently used by other authors to assess the state of the art of
applications of machine learning in other astronomical subfields. For
comparison, applications of machine learning to Solar System bodies, a larger
area that includes imaging and spectrophotometry of small bodies, have already
reached a state classified as progressing. Research communities and
methodologies are more established, and the use of ML led to the discovery of
new celestial objects or features. ML applied to asteroid dynamics, however, is
still in the emerging phase, with smaller groups, and fewer papers producing
discoveries. Large observational surveys, like those conducted at the Vera C.
Rubin Observatory, will produce very substantial datasets of orbital and
physical properties for asteroids. Applications of ML for clustering, image
identification, and anomaly detection, among others, are currently being
developed and are expected of being of great help.
