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Many of the problems in civil and systems engineering, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical model framework provides a unified view for this wide range of problems, enables efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for applying graphical models and causality to model complex networked engineering systems.