Difference between revisions of "Template:NextTalk"

Revision as of 12:46, 3 April 2020

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-UPDATE: This lecture series has been cancelled due to CoV-19
+UPDATE: The lecture series by Nina Otter has been cancelled due to CoV-19 policies
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 <b>Abstract</b>
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-Techniques and ideas from topology - the mathematical area that studies shapes - are being applied to the study of data with increasing frequency and success.
-In this lecture series we will explore how we can use homology, a technique in topology that gives a measure of the number of holes of a space, to study data. The most well-known method of this type is persistent homology, in which one associates a one-parameter family of spaces to a data set and studies how the holes evolve across the parameter space. A more recent and less well-known technique is magnitude homology, which one can think of as giving a measure of the "effective number of points" of a metric space. In this course we will introduce the theoretical background for persistent and magnitude homology, and then dive into applications using software implementations and statistical analysis tools on real-world data sets.
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