  Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers
(VCSELs) are the main workhorse within data centers, supercomputers, and even
vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under
extremely harsh and time-varying conditions, thus requiring adaptive and
flexible designs of the communication chain. Such designs can be built based on
mathematical models (model-based design) or learned from data (machine learning
(ML) based design). Various ML techniques have recently come to the forefront,
replacing individual components in the transmitters and receivers with deep
neural networks. Beyond such component-wise learning, end-to-end (E2E)
autoencoder approaches can reach the ultimate performance through co-optimizing
entire parameterized transmitters and receivers. This tutorial paper aims to
provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches,
dealing specifically with the unique challenges facing VCSELs, such as the wide
temperature variations and complex models.
