Название: Linear Algebra With Machine Learning and Data
Автор: Crista Arangala
Издательство: CRC Press
Год: 2023
Страниц: 310
Язык: английский
Формат: pdf (true), epub
Размер: 21.1 MB

This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation. Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant case studies using real-world data. All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics. A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.

The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation. Chapters 1 and 3 focus on eigenvalues and singular values, as well as their associated vectors, that are of key importance in clustering techniques, such as Principal Component Analysis. Chapters 4 through 6 focus on techniques specific to interpolation models. Integrated throughout this text, the reader will also find many Machine Learning techniques of interest, such as hidden Markov chains in Chapter 2, neural networks in Chapter 5, decision trees and forests in Chapter 6, and random matrices in Chapter 7.