This robustifying machine learning (ML) for cyber-physical systems (CPSs) project focuses on detecting and reducing the vulnerabilities of ML models that have become pervasive and are being deployed for decision-making in real-life CPS applications including self-driving cars, and robotic air vehicles. The growing prospect of machine learning approaches such as deep Convolutional Neural Networks (CNN) and deep Reinforcement Learning (DRL) being used in CPSs (e.g., self-driving cars) has raised concerns around safety and robustness of autonomous agents. Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a deep learning (DL) model dramatically. Apart from adversarial attacks, such DL models can also succumb to the so-called 'edge-cases' where the real-life operational situation presents data that are not well-represented in the training data set. Such cases have been the primary reason for quite a few self-driving car accidents recently. Although initial research has begun to address scenarios with specific attack models, there remains a significant knowledge gap regarding detection and adaptation of ML models to 'edge-cases' and adversarial attacks in the context of CPS. With this motivation, this project builds a meta-learning-based supervisory framework and associated algorithms to detect and mitigate ML system vulnerabilities which will substantially reduce the risk in using ML for safety and time-critical systems. The science driver applications are self-driving cars and robotics. The algorithm validation and evaluation use experimental self-driving cars and robotics test beds at Iowa State in collaboration with the Institution of Transportation and NVIDIA. Research is integrated with education to support the goal of training students in the critical interdisciplinary area of system theory and data science, which is in dire need of rapid and quality workforce development for sustained economic and social growth of the United States. Education plans also include curriculum development at graduate and undergraduate level, undergraduate research experience, academic competitions and outreach activities involving both high school students and teachers. Outcomes of this project will support NSF's mission of "Harnessing the Data Revolution" for many critical CPSs that currently involve ML or will involve it in future, such as manufacturing processes, power grid, smart cities and transportation systems, to make them safer, more efficient and cost effective.