will talk about 'Crafting Topological Features for Machine Learning Pipelines'
Abstract: The field of topological data analysis (TDA) has exploded in the last twenty years. This suite of tools creates methods for quantifying shape in data by incorporating ideas from a wide range of subjects such as topology, geometry, algebra, category theory, and graph theory. In this talk we will discuss the basic setup of some of main tools in TDA, how these can be fit into an ML pipeline, and show example applications highlighting the kinds of structures that can be found with these methods.
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