Exploring Linear Algebra. Questo articolo non è disponibile.Exploring Linear Algebra

This text focuses on the primary topics in a first course in Linear Algebra. The author includes additional advanced topics related to data analysis, singular value decomposition, and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica® demonstrations and Mathematica® coding.

The book includes interesting examples embedded in the projects. Examples include the discussions of “Lights Out”, Nim, the Hill Cipher, and a variety of relevant data science projects.

The 2nd Edition contains:

• Additional Theorems and Problems for students to prove/disprove (these act as theory exercises at the end of most sections of the text)
• Additional sections that support Data Analytics techniques, such as Kronecker sums and products, and LU decomposition of the Vandermonde matrix
• Updated and expanded end-of-chapter projects

Instructors and students alike have enjoyed this popular book, as it offers the opportunity to add Mathematica® to the Linear Algebra course.

I would definitely use the book (specifically the projects at the end of each section) to motivate undergraduate research.―Nick Luke, North Carolina A&T State University.

Dr. Crista Arangala is Professor of Mathematics and Chair of the Department of Mathematics and Statistics at Elon University in North Carolina. She has been teaching and researching in a variety of fields, including inverse problems, applied partial differential equations, applied linear algebra, mathematical modeling, and service learning education. She runs a traveling science museum with her Elon University students in Kerala, India. Dr. Arangala was chosen to be a Fulbright Scholar in 2014 as a visiting lecturer at the University of Colombo where she continued her projects in inquiry learning in Linear Algebra and began working with a modeling team focusing on Dengue fever research. Dr. Arangala has published several textbooks that implore inquiry learning techniques, including Exploring Linear Algebra: Labs and Projects with MATLAB®, Mathematical Modeling: Branching Beyond Calculus, and Linear Algebra with Machine Learning and Data.