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April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Representing knowledge through graphical models

This will be presented in English.

Ruiwen Zhang (SAS Institute)
13:1013:50 Thursday, April 12, 2018
模型与方法 (Models and Methods), 英文讲话 (Presented in English)
Location: 报告厅(Auditorium) Level: Intermediate
Secondary topics:  AI应用的硬件、软件栈(Hardware and Software stack for AI applications)
Tags: wl

您将学到什么 (What you'll learn)

Understand theories and methods for graphical models and their applications

描述 (Description)

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 properties, which can be further used to design and motivate new models, and how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models.

Photo of Ruiwen Zhang

Ruiwen Zhang

SAS Institute

Ruiwen Zhang is a senior research statistician at SAS, focusing 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.