With increasing the complexity of digital systems and the use of advanced nanoscale technology nodes, various process and runtime variabilities threaten the correct operation of these systems. The interdependence of these reliability detractors and their dependencies to circuit structure as well as running workloads makes it very hard to derive simple deterministic models to analyze and target them. As a result, machine learning techniques can be used at circuit and gate level to extract useful information which can be used to effectively monitor and improve the reliability of digital systems. These learning schemes are typically performed offline on large data sets in order to obtain various regression models which then are used during runtime operation to predict the health of the system and guide appropriate adaptation and countermeasure schemes. The purpose of this session is to discuss and evaluate various learning schemes in order to analyze the reliability of the system due to various runtime failure mechanisms which originate from process and runtime variabilities such as thermal and voltage fluctuations, device and interconnect aging mechanisms, as well as radiation-induced soft errors.