The success of machine learning has recently motivated researchers in formal methods to adapt the highly scalable learning methods to the verification setting, where correctness guarantees on the result are essential. The aim of this workshop is to bring together researchers from the formal verification community that are developing approaches to exploit learning methods in verification as well as researchers from machine learning area interested in applications in verification and synthesis. The general topic of machine learning in verification includes, for instance,
the use of learning techniques (e.g. reinforcement learning) for speeding up verification (e.g. rigorous analysis of complex systems combining non-determinism, stochasticity, timing etc.),
the use of machine learning data structures and algorithms (e.g. decision trees) for enhancing results of verification (e.g. generating simple invariants of programs generating small controllers of systems),
verification of machine-learning artefacts (e.g. verification of neural networks), or
meta-usage of machine learning (e.g. to predict the best tools to be applied to a verification problem).