Abstract
As the title suggests, this thesis is devoted to machine learning algorithms for stochastic control with applications in mathematical finance.
In the first two chapters, we utilize neural networks to find the optimal exercise strategies of high-dimensional American options. Moreover, we compute option values along stochastic trajectories of the underlying
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