Machine Learning has produced a widespread family of prediction models that are currently deployed in diverse sectors, ranging from autonomous driving to internet security realms. At the same time, machine learning models are vulnerable to malicious attacks in the form of perturbing the data for the sake of deteriorating the predictive performance of the algorithms. This seminar focuses on discussing cutting-edge research publications dedicated to developing adversarial attacks, as well as respective defense mechanisms for machine learning models.