Fundamentals of Abstract Algebra (Textbooks in Mathematics)

DeBonis, Mark J.

ISBN 10: 1032370912 / ISBN 13: 9781032370910

Published by Chapman and Hall/CRC, 2024

About this Item

Seller Inventory # I-9781032370910

Bibliographic Details

• Title: Fundamentals of Abstract Algebra (Textbooks in Mathematics)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2024
• Language: English
• ISBN 10: 1032370912
• ISBN 13: 9781032370910
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Fundamentals of Abstract Algebra is a primary textbook for a one year first course in Abstract Algebra, but it has much more to offer besides this. The book is full of opportunities for further, deeper reading, including explorations of interesting applications and more advanced topics, such as Galois theory. Replete with exercises and examples, the book is geared towards careful pedagogy and accessibility, and requires only minimal prerequisites. The book includes a primer on some basic mathematical concepts that will be useful for readers to understand, and in this sense the book is self-contained.

Features

• Self-contained treatments of all topics
• Everything required for a one-year first course in Abstract Algebra, and could also be used as supplementary reading for a second course
• Copious exercises and examples

Mark 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, both as an applied mathematician of machine learning. He held a position as Associate Professor of Mathematics at Manhattan College in New York City, but later left to pursue research working for the US Department of Energy at Sandia National Laboratory as a Principal Data Analyst. His research interests include machine learning, statistics and computational algebra.

About the Author

Mark DeBonis received his PhD in Mathematics from 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 both as an applied mathematician of machine learning. He held a position as Associate Professor of Mathematics at Manhattan College in New York City, but later left to pursue research working for the US Department of Energy at Sandia National Laboratory as a Principal Data Analyst. His research interests include machine learning, statistics and computational algebra.