| Summary Note |
"This book is on the mathematics of data science, thus the mathematical perspective will shape the presentation of the material, without forgetting the data science driver behind it. Three important themes of data science are data reduction and visualization, clustering and classification. It is well known that behind these and numerous other techniques in data science there is mathematics that makes things work. In this book, these themes are approached, mainly, but not solely, from a linear algebra point of view. With this perspective in mind, the present book consists of a preface and twelve chapters, while at the end of the book the bibliography and an index are provided. Moreover, at the end of each chapter some notes and comments are given, which will help a reader wishing to pursue the area of each chapter further. In the sequel, the subject of each one of the twelve chapters is briefly presented."-- provided by publisher: |
