Elenco seminari del ciclo di seminari
“TOPOLOGICAL DATA ANALYSIS AND PERSISTENT HOMOLOGY”

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)

The aim of this course is to introduce topological data analysis (TDA) and its main algorithm, persistent homology (PH). PH is a computational tool, whose aim is to study the “shape” of data via topological techniques. PH has been used with increasing frequency and success to quantify the structure of complex data. Among other applications, PH has enabled novel insights in cancer studies, pathology, evolution, ecology and material science. In this course we will look at the standard PH pipeline, a number of different software programs and computational tools, some real-world applications, and some theoretical generalisations (multiparameter persistence, zig-zag persistence, etc.)