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Explore advanced mathematical concepts in this 50-minute seminar presentation delivered by Luc Brogat-Motte from Istituto Italiano di Tecnologia (IIT). Delve into the theoretical foundations and practical applications of controlled stochastic differential equations, examining how machine learning techniques can be applied to learn and model these complex mathematical systems. Discover the intersection of statistical methods and machine learning approaches for handling prediction uncertainty in stochastic processes. Learn about calibration techniques and methods for leveraging uncertainty in mathematical modeling, with particular focus on controlled systems governed by stochastic differential equations. Gain insights into cutting-edge research methodologies that bridge traditional statistical approaches with modern machine learning frameworks for understanding and predicting behavior in uncertain dynamical systems.
