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This course teaches an overview of modern (randomized) iterative methods for applications in machine learning and data science. In particular, we will discuss the theoretical basics of stochastic optimization, iterative algorithms for variational inequalities, scalability of algorithms to large datasets, and challenges in distributed optimization, such as decentralized or federated machine learning. We will cover a set of foundational papers and a selection of recent publications.