Seminar Topological Data Analysis

Dozent :: Dr. Bernadette Stolz
Übungsleitung :: Sophie Rosenmeier, Britta Daub, Juliette Murris, Alexandros Xenos
Stunden pro Woche :: 2
ECTS:: 3
Sprache :: English

Lehrplan

Students in this course will learn the fundamentals of topological data analysis (TDA). They will then apply this knowledge to real or synthetic data of their choice, with a focus on (simple) biological applications. In doing so, students will develop an understanding of:

the potential and limitations of TDA methods,
relevant theoretical concepts and questions.

Potential project topics include the Mapper algorithm, the Vietorisâ€“Rips complex and filtrations for weighted networks, cubical complexes for imaging data, relational/chromatic persistent homology for labelled point clouds, as well as vectorisation techniques and stability results.

Struktur

The course consists of two parts.

Learning phase: After an introduction, the students form groups and choose a topic from a provided list. Supported by suggested literature and a group mentor, they explore their topic, identify suitable real or synthetic datasets, and conduct independent analyses. Regular support is provided by the group mentor, along with mandatory progress sessions held on Thursdays at 10 am.
Conference day: Students independently implement methods from TDA and conduct data analyses. They present their results on the final day of the seminar, which is organised as a conference day.

Zeit und Ort

Donnerstag 10:00-12:00 c.t. in Frauenlobstr. 7A, Raum 257*

The course finds place in person in an irregular rythm based on previous agreement with the lecturer

Benotung

Die Note basiert auf der Teilnahme an Sitzungen mit Gruppenmentoren und Aktualisierungstreffen (20%), dem Poster (40%) und der PrÃ¤sentation (40%).

Das Poster muss im A1-Format erstellt und in einer 5-minÃ¼tigen PrÃ¤sentation vorgestellt werden.
Die AbschlussprÃ¤sentation muss 10-20 Minuten dauern.