Graphical models have enjoyed a surge of interest in many areas of machine learning recently due to their ability to learn and perform inference in large networks. Prominent applications of graphical models include studying functional connectivity among brain regions, exploring the regulatory relationships between genes, and recommending the most related information from millions of items.
Drawing on several real-world cases, Ruiwen Zhang demonstrates how to visualize the structure of a probabilistic model and provide better insights into the model’s properties, which can be further used to design and motivate new models. She also explains how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models.
Ruiwen Zhang is a senior research statistician at SAS, where she focuses on machine learning and data mining. She holds a PhD from the Department of Statistics and Operation Research at the University of North Carolina at Chapel Hill.
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