EP3260: Fundamentals of Machine Learning Over Networks

This course covers fundamentals of machine learning over networks (MLoN). It starts from a conventional single-agent setting where one server runs a convex/non-convex optimization problem to learn an unknown function. We introduce several approaches to address this seemingly, simple yet fundamental, problem. We introduce an abstract form of MLoN, present centralized and distributed solution approaches to address this problem, and exemplify via training a deep neural network over a network. The course covers various important aspects of MLoN, including optimality, computational complexity, communication complexity, security, large-scale learning, online learning, MLoN with partial information, and several application areas. As most of these topics are under heavy researches nowadays, the course is not based on a single textbook but builds on a series of key publications in the field. The course also includes a two-days workshop on recent advancements on fundamentals of MLoN. The course started in 2019 and the previous course material can be found here.

Students will be grouped for homework and computer assignments. Special topic sessions are in the format of a two-days workshop, where students will present (both oral presentation and posters) some key publications of the field. A basic knowledge of convex optimization and probability theory is required to follow the course.

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