Topological Data Analysis (TDA) is a mathematical framework focused on studying and quantifying the “shape” of data. Its primary goal is to describe and measure the similarity in datasets by using distances, particularly when equivalences are defined through geometric transformations. Additionally, TDA is highly effective for reducing the dimensionality of data, making it easier to analyze and compare. It can also be utilized in geometric machine learning, and its approach can be applied to a wide range of data types, including time series, 2D and 3D objects, and point clouds. Throughout the course, fundamental concepts required for a basic understanding of TDA will be introduced, with a focus on practical and computational examples, rather than formal mathematical theory.
- Teacher: PATRIZIO FROSINI