Following a series of crippling cyber-attacks that targeted major of the public and social sectors — including schools, hospitals, critical infrastructure, and private businesses — the global community has increased its attention on the wider societal impacts of major cyber security events, forming task forces like the UN Open Ended Working Group on Cyber Security and undertaking policy efforts to mitigate these impacts. These actions are important, but policy changes only represent one side of the solution. On the other are technical developments, within which machine learning has been proposed as a key component of future of cyber defense tools, requiring rapid development to provide the speed and scale needed to detect and respond to new and emerging cyber security threats. Cybersecurity is inherently a systems problem and piece-wise application of off-the-shelf ML tools leave critical gaps in both sophistication and interpretable context needed for comprehensive security systems. To successfully develop ML-based cybersecurity defenses, a greater degree of cross-pollination across the ML and cybersecurity communities is needed because both are highly specialized technical domains. Moreover, the requisite ML topics needed to successfully leverage ML for cybersecurity — such as time series analytics, game theory, deep learning, reinforcement learning, representation learning, semi-supervised and self-supervised learning, learning on large scale streaming data, interpretable and robust autonomous systems, etc. - are foundational to the ICML community.The primary aim of this workshop is to build a mutual comprehensive awareness of the problem and solution spaces across the greater ML community and the Cybersecurity/ML for Cybersecurity communities. To provide meaningful engagement, workshop organizers will curate a program which defines the interdisciplinary boundary and opportunities between machine learning and cybersecurity.