  Machine Learning (ML) for Optical Communication (OC) is certainly a hot topic
emerged recently and will continue to raise interest at least for the next few
years. The rate of research development in this area is growing very rapidly.
Novelty of this research direction resides mainly in the peculiarity of the
application field, rather than in the methodological approaches, which are (at
least up to now) state-of-the-art ML algorithms. Reviewing the literature shows
that many of the ML algorithms have not yet been used in this area, and many of
the OC applications are not considered yet, which reflects the fact that the
research topic is pristine. Accordingly, tutorial investigations are quiet
necessary in this filed to help researchers be aware about the last
progressions and cavities of this field. Although several tutorials have been
released recently, they considered this topic from OC view, and neglected ML
view. However, it is required to have an investigations about the ML algorithms
used in this subject. Accordingly, for the first time, this paper reviews ML
for OC literature from ML viewpoint. This view could be really helpful because
only OC experts work on ML for OC, and they are not ML experts, so it could
really help them to have a comprehensive view on the ML subjects implantable in
OC. It has worth to mention that compared with other works, this survey reviews
much more investigations; therefore, it has more generality, and gives the
reader to have a comprehensive overview on this topic.
