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Learning and Extrapolation in Graph Neural Networks

Offered By: IEEE Signal Processing Society via YouTube

Tags

Data Science Courses Machine Learning Courses Signal Processing Courses Network Analysis Courses Predictive Modeling Courses

Course Description

Overview

Explore the intricacies of learning and extrapolation in Graph Neural Networks through this comprehensive webinar presented by Stefanie Jegelka from MIT. Delve into cutting-edge research and insights as part of the Data sciEnce on GrAphS (DEGAS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Gain valuable knowledge about the latest advancements in graph-based machine learning techniques and their applications in various domains. Discover how Graph Neural Networks can be leveraged to tackle complex problems and improve predictive capabilities in networked data structures.

Syllabus

Learning and Extrapolation in Graph Neural Networks


Taught by

IEEE Signal Processing Society

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