Introduction To Linear Algebra (Paperback)

Mark J. DeBonis

ISBN 10: 1032109386 / ISBN 13: 9781032109381

Edité par Taylor & Francis Ltd, 2024

A propos de cet article

Paperback. Introduction to Linear Algebra: Computation, Application, and Theory is designed for students who have never been exposed to the topics in a linear algebra course. The text is lled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way. After completing the course with this text, the student will not only know the best and shortest way to do linear algebraic computations but will also know why such computations are both eective and successful.Features: Includes cutting edge applications in machine learning and data analytics Suitable as a primary text for undergraduates studying linear algebra Requires very little in the way of pre-requisites This book is designed for students who have never been exposed to the topics in a linear algebra course. The text is lled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9781032109381

Détails bibliographiques

• Titre: Introduction To Linear Algebra (Paperback)
• Éditeur: Taylor & Francis Ltd
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 1032109386
• ISBN à 13 chiffres: 9781032109381
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

This book is designed for students who have never been exposed to the topics in a linear algebra course. The text is filled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way.

À propos de l?auteur

Mark J. DeBonis received his PhD in Mathematics from the University of California, Irvine, USA. He began his career as a theoretical mathematician in the field of group theory and model theory, but in later years switched to applied mathematics, in particular to machine learning. He spent some time working for the US Department of Energy at Los Alamos National Lab as well as the US Department of Defense at the Defense Intelligence Agency as an applied mathematician of machine learning. He is an Associate Professor of Mathematics at Manhattan College in New York City and is also currently working for the US Department of Energy at Sandia National Lab as a Principal Data Analyst. His research interests include machine learning, statistics, and computational algebra.