Modern society increasingly relies on machine learning (ML) solutions. Like other systems, ML systems must meet their requirements. Standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage or traditional software debugging become practically irrelevant for ML systems. This is due to the nondeterministic nature of ML systems, reuse of high quality implementations of ML algorithms, and lack of understanding of the semantics of learned models, for example, when deep learning methods are applied.For example, self-driving car models may have been learned in a cold weather country. When such a car is deployed in a hot weather country, it will likely face dramatically different driving conditions that may render its models obsolete. This calls for novel methods and new methodologies and tools to address quality and reliability challenges of ML systems.Furthermore, broad deployment of ML software in networked systems inevitably exposes the ML software to attacks. While classical security vulnerabilities are relevant, ML techniques have additional weaknesses, some already known (for example, sensitivity to training data manipulation), and some yet to be discovered. Hence, there is a need for research as well as practical solutions to ML security problems.With these in mind, this workshop solicits original contributions addressing problems and solutions related to dependability, quality assurance and security of ML systems. The workshop combines several disciplines, including ML, software engineering (with emphasis on quality), security, and algorithmic game theory. It further combines academia and industry in a quest for well-founded practical solutions.