  Machine learning (ML) pervades an increasing number of academic disciplines
and industries. Its impact is profound, and several fields have been
fundamentally altered by it, autonomy and computer vision for example;
reliability engineering and safety will undoubtedly follow suit. There is
already a large but fragmented literature on ML for reliability and safety
applications, and it can be overwhelming to navigate and integrate into a
coherent whole. In this work, we facilitate this task by providing a synthesis
of, and a roadmap to this ever-expanding analytical landscape and highlighting
its major landmarks and pathways. We first provide an overview of the different
ML categories and sub-categories or tasks, and we note several of the
corresponding models and algorithms. We then look back and review the use of ML
in reliability and safety applications. We examine several publications in each
category/sub-category, and we include a short discussion on the use of Deep
Learning to highlight its growing popularity and distinctive advantages.
Finally, we look ahead and outline several promising future opportunities for
leveraging ML in service of advancing reliability and safety considerations.
Overall, we argue that ML is capable of providing novel insights and
opportunities to solve important challenges in reliability and safety
applications. It is also capable of teasing out more accurate insights from
accident datasets than with traditional analysis tools, and this in turn can
lead to better informed decision-making and more effective accident prevention.
