Difference between revisions of "Template:NextTalk"
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Topological Data Analysis
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| − | [ | + | [https://www.mathi.uni-heidelberg.de/~mbleher/journal_club_TDA.html| Topological Data Analysis]<br> |
| − | + | Mon 11h15-12h45, Zoom <br> | |
organizers: Michael Bleher, Max Schmahl, Daniel Spitz | organizers: Michael Bleher, Max Schmahl, Daniel Spitz | ||
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<b>Abstract:</b> Persistent theory is a useful tool to extract information from real-world data sets, and profits of techniques from different mathematical disciplines, such as Morse theory and quiver representation. In this seminar, I am going to present a new approach for studying persistence theory using model categories. I will briefly introduce model categories and then describe how to define a model structure on the category of the tame parametrised chain complexes, which are chain complexes that evolve in time, describing why such an approach can be useful in topological data analysis. In particular, I will use the model category structure to retrieve two invariants that extract homotopical and homological information from any tame parametrised chain complex. | <b>Abstract:</b> Persistent theory is a useful tool to extract information from real-world data sets, and profits of techniques from different mathematical disciplines, such as Morse theory and quiver representation. In this seminar, I am going to present a new approach for studying persistence theory using model categories. I will briefly introduce model categories and then describe how to define a model structure on the category of the tame parametrised chain complexes, which are chain complexes that evolve in time, describing why such an approach can be useful in topological data analysis. In particular, I will use the model category structure to retrieve two invariants that extract homotopical and homological information from any tame parametrised chain complex. | ||
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| + | <h3> | ||
| + | <p>Python Course on <br> | ||
| + | Topological Methods in Data Analysis<br> | ||
| + | Heidelberg University </p> | ||
| + | <p> October 26th - 28th <br> | ||
| + | Mathematikon and Zoom <br> </p> | ||
| + | </h3> | ||
| + | <div style="text-align:center"> | ||
| + | <i> | ||
| + | <b>Abstract:</b> | ||
| + | In this twelve-hour workshop the participants will be introduced to the main techniques utilized in topological data analysis and their implementation provided by the python package scikit-tda. Introductions to the Mapper algorithm and persistent homology will be complemented by respective hands-on tutorial sessions. The workshop will conclude with an exploratory project of these methods on ‘real data’, which may be provided by the participants. | ||
| + | </i> | ||
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| + | More information on the [https://micbl.github.io/TDAworkshop/ course website] | ||
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Revision as of 12:50, 10 November 2020
Journal Club
Topological Data Analysis
Mon 11h15-12h45, Zoom
organizers: Michael Bleher, Max Schmahl, Daniel Spitz