A Beginner?s Guide to Mathematical Proof - DeBonis, Mark J.; - Prospero Internet Bookshop

A Beginner?s Guide to Mathematical Proof
 
Product details:

ISBN13:9781032686196
ISBN10:1032686197
Binding:Hardback
No. of pages:170 pages
Size:234x156 mm
Weight:453 g
Language:English
Illustrations: 23 Illustrations, black & white; 23 Line drawings, black & white; 31 Tables, black & white
700
Category:

A Beginner?s Guide to Mathematical Proof

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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Short description:

A Beginner?s Guide to Mathematical Proof prepares mathematics majors for the transition to abstract mathematics, and introduces a wider readership of quantitative science students to the mathematical structures underlying more applied topics with an accessible, step-by-step approach requiring minimal mathematical prerequisites.

Long description:

A Beginner?s Guide to Mathematical Proof prepares mathematics majors for the transition to abstract mathematics, as well as introducing a wider readership of quantitative science students, such as engineers, to the mathematical structures underlying more applied topics.


The text is designed to be easily utilized by both instructor and student, with an accessible, step-by-step approach requiring minimal mathematical prerequisites. The book builds towards more complex ideas as it progresses but never makes assumptions of the reader beyond the material already covered.


Features


? No mathematical prerequisites beyond high school mathematics


? Suitable for an Introduction to Proofs course for mathematics majors and other students of quantitative sciences,   such as engineering


? Replete with 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 as an applied mathematician of machine learning. He is at present working for the US Department of Energy at Sandia National Lab. His research interests include machine learning, statistics, and computational algebra.

Table of Contents:

Preface,  Chapter 1 Mathematical Logic,  Chapter 2 Methods of Proof,  Chapter 3 Special Proof Types,  Chapter 4 Foundational Mathematical Topics,  References,  Index