Introduction To Linear Algebra: Computation, Application, and Theory

Author:   Mark J. DeBonis (Manhattan College, USA)
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032109381


Pages:   420
Publication Date:   27 May 2024
Format:   Paperback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Introduction To Linear Algebra: Computation, Application, and Theory


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Author:   Mark J. DeBonis (Manhattan College, USA)
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.800kg
ISBN:  

9781032109381


ISBN 10:   1032109386
Pages:   420
Publication Date:   27 May 2024
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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"""Exceptionally well organized and thoroughly 'student friendly' in presentation, Introduction To Linear Algebra: Computation, Application, and Theory is an ideal textbook for highschool, college, and university curriculums"" - Midwest Books Review"


Author Information

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.

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