Linear Algebra Done Wrong
by Sergei Treil
Number of pages: 222
The title of the book sounds a bit mysterious. Why should anyone read this book if it presents the subject in a wrong way? What is particularly done "wrong" in the book? Before answering these questions, let me first describe the target audience of this text. This book appeared as lecture notes for the course "Honors Linear Algebra". It supposed to be a first linear algebra course for mathematically advanced students. It is intended for a student who, while not yet very familiar with abstract reasoning, is willing to study more rigorous mathematics that is presented in a "cookbook style" calculus type course. Besides being a first course in linear algebra it is also supposed to be a first course introducing a student to rigorous proof, formal definitions---in short, to the style of modern theoretical (abstract) mathematics. The target audience explains the very specific blend of elementary ideas and concrete examples, which are usually presented in introductory linear algebra texts with more abstract definitions and constructions typical for advanced books.
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by Marcel B. Finan - Arkansas Tech University
This book is addressed primarely to second and third year college students who have already had a course in calculus and analytic geometry. Its aim is solely to learn the basic theory of linear algebra within a semester period.
by Ruslan Sharipov - Samizdat Press
This is a textbook of multidimensional geometry and linear algebra for the first year students. It covers linear vector spaces and linear mappings, linear operators, dual space, bilinear and quadratic forms, Euclidean spaces, Affine spaces.
by Andrew Baker - University of Glasgow
The text covers basic ideas and techniques of Linear Algebra that are applicable in many subjects including the physical and chemical sciences, and statistics. These notes were originally written for a course at the University of Glasgow.
by W W L Chen - Macquarie University
Linear equations, matrices, determinants, vectors, vector spaces, eigenvalues and eigenvectors, linear transformations, real inner product spaces, orthogonal matrices, applications of real inner product spaces, complex vector spaces.