  Thanks to the recent advances in processing speed and data acquisition and
storage, machine learning (ML) is penetrating every facet of our lives, and
transforming research in many areas in a fundamental manner. Wireless
communications is another success story -- ubiquitous in our lives, from
handheld devices to wearables, smart homes, and automobiles. While recent years
have seen a flurry of research activity in exploiting ML tools for various
wireless communication problems, the impact of these techniques in practical
communication systems and standards is yet to be seen. In this paper, we review
some of the major promises and challenges of ML in wireless communication
systems, focusing mainly on the physical layer. We present some of the most
striking recent accomplishments that ML techniques have achieved with respect
to classical approaches, and point to promising research directions where ML is
likely to make the biggest impact in the near future. We also highlight the
complementary problem of designing physical layer techniques to enable
distributed ML at the wireless network edge, which further emphasizes the need
to understand and connect ML with fundamental concepts in wireless
communications.
