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The homology of Data <br> | The homology of Data <br> | ||
Nina Otter (UCLA) | Nina Otter (UCLA) | ||
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14.04. - 17.04.2020 <br> | 14.04. - 17.04.2020 <br> | ||
Physikalisches Institut, INF 226 | Physikalisches Institut, INF 226 | ||
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part of the [https://gsfp.physi.uni-heidelberg.de/graddays/| Heidelberg Physics Graduate Days] | part of the [https://gsfp.physi.uni-heidelberg.de/graddays/| Heidelberg Physics Graduate Days] | ||
Revision as of 12:31, 3 April 2020
UPDATE: This lecture series has been cancelled due to CoV-19
The homology of Data
Nina Otter (UCLA)
14.04. - 17.04.2020
Physikalisches Institut, INF 226
part of the Heidelberg Physics Graduate Days
Abstract 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.