Название: Algebraic Foundations for Applied Topology and Data Analysis
Автор: Hal Schenck
Издательство: Springer
Серия: Mathematics of Data
Год: 2022
Страниц: 231
Язык: английский
Формат: pdf (true), epub
Размер: 18.57 MB
This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. Examples of potential topics include optimization, topological data analysis, compressed sensing, algebraic statistics, information geometry, manifold learning, tensor decomposition, support vector machines, neural networks, and many more. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.
Автор: Hal Schenck
Издательство: Springer
Серия: Mathematics of Data
Год: 2022
Страниц: 231
Язык: английский
Формат: pdf (true), epub
Размер: 18.57 MB
This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. Examples of potential topics include optimization, topological data analysis, compressed sensing, algebraic statistics, information geometry, manifold learning, tensor decomposition, support vector machines, neural networks, and many more. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.