This book is
meant to provide an introduction to vectors, matrices, and least squares
methods, basic topics in applied linear algebra. Our goal is to give the
beginning student, with little or no prior exposure to linear algebra, a good
grounding in the basic ideas, as well as an appreciation for how they are used
in many applications, including data fitting, machine learning and artificial
intelligence, tomography, image processing, finance, and automatic control
systems. Topics covered includes: Vectors, Norm and distance, Clustering,
Matrices, Linear equations, Matrix multiplication, Linear dynamical systems,
Least squares, Multi-objective least squares, Constrained least squares.

This book is
meant to provide an introduction to vectors, matrices, and least squares
methods, basic topics in applied linear algebra. Our goal is to give the
beginning student, with little or no prior exposure to linear algebra, a good
grounding in the basic ideas, as well as an appreciation for how they are used
in many applications, including data fitting, machine learning and artificial
intelligence, tomography, image processing, finance, and automatic control
systems. Topics covered includes: Vectors, Norm and distance, Clustering,
Matrices, Linear equations, Matrix multiplication, Linear dynamical systems,
Least squares, Multi-objective least squares, Constrained least squares.

The purpose with
these notes is to introduce students to the concept of proof in linear algebra
in a gentle manner. Topics covered includes: Matrices and Matrix Operations,
Linear Equations, Vector Spaces, Linear Transformations, Determinants, Eigenvalues and Eigenvectors, Linear Algebra and Geometry.

This book explains the following topics related to Linear Algebra: Vectors, Linear Equations, Matrix Algebra, Determinants, Eigenvalues and
Eigenvectors, Linear Transformations, Dimension, Similarity and
Diagonalizability, Complex Numbers, Projection Theorem, Gram-Schmidt
Orthonormalization, QR Factorization, Least Squares Approximation, Orthogonal
(Unitary) Diagonalizability, Systems of Differential Equations, Quadratic Forms,
Vector Spaces and the Pseudoinverse.

This
note emphasize the concepts of vector spaces and linear transformations as
mathematical structures that can be used to model the world around us. Topics
covered includes: Gaussian Elimination, Elementary Row Operations, Vector
Spaces, Linear Transformations, Matrices, Elementary Matrices and Determinants,
Eigenvalues and Eigenvectors, Diagonalization, Kernel, Range, Nullity, Rank,
Gram-Schmidt and Orthogonal Complements.

This
is a text for a basic course in algebraic number theory. This book covers the following topics:
Norms, Traces and Discriminants, Dedekind Domains, Factoring of Prime Ideals in
Extensions, The Ideal Class Group, The Dirichlet Unit Theorem, Cyclotomic
Extensions, Factoring of Prime Ideals in Galois Extensions and Local Fields

Author(s): Robert
B. Ash, Professor Emeritus, Mathematics

This book covers the
following topics: Pari Types, Transcendental and Other Nonrational Functions,
Arithmetic Functions, Polynomials and Power Series, Sums, Products and
Integrals, Basic Programming, Algebraic Number Theory and Elliptic Curves.