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Linear Algebra in Twenty Five Lectures (PDF 395P)
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.
Author(s): Tom Denton and Andrew Waldron
395 Pages 
Linear Algebra Lecture Notes by Cesar
This note covers systems of linear equations, Row reduction and echelon form, Vector equations, The matrix equation, Homogeneous and nonhomogeneous systems, Linear independence, Introduction to linear mappings, Onto, One to one and standard matrix, Matrix algebra, Invertible matrices, Determinants, Properties of the determinant, Applications of the determinant, Vector spaces, Linear maps, Linear independence, Bases and dimension, The rank theorem, Coordinate systems, Change of basis, Inner products and orthogonality, Eigen values and eigen vectors, The characteristic polynomial, Diagonalization, Diagonalization of symmetric matrices, The PageRank algorithm, Discrete dynamical systems.
Author(s): Cesar O Aguilar, Suny Geneseo
206 Pages
This books covers the following topics: Linear Systems, Vector Spaces, Maps Between Spaces, Determinants, Similarity.
Author(s): Jim Hefferon, Mathematics Department, Saint Michael's College
525 Pages
A First Course in Linear Algebra by Ilijas Farah and K. Kuttler
The contents of this book include: Systems of Equations, Matrices, Determinants, Linear Transformations, Complex Numbers, Spectral Theory, Some Curvilinear Coordinate Systems, Vector Spaces.
Author(s): Ilijas Farah, K. Kuttler
608 Pages
A Brief Introduction to Linear Algebra
This note covers the following topics: Linear Algebra, Matrix Algebra, Homogeneous Systems and Vector Subspaces, Basic Notions, Determinants and Eigenvalues, Diagonalization, The Exponential of a Matrix, Applications,Real Symmetric Matrices, Classification of Conics and Quadrics, Conics and the Method of Lagrange Multipliers, Normal Modes.
Author(s): Leonard Evens
NA Pages
Introduction to Applied Linear Algebra
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.
Author(s): Stephen Boyd and Lieven Vandenberghe
473 Pages
Linear Algebra Notes by David A. Santos
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.
Author(s): David A. Santos
270 Pages
Linear Algebra, Theory And Applications
This is a book on linear algebra and matrix theory. It provides an introduction to various numerical methods used in linear algebra. This is done because of the interesting nature of these methods. Topics covered includes: Matrices And Linear Transformations, Determinant, Row Operations, Factorizations, Vector Spaces And Fields, Linear Transformations, Inner Product Spaces, Norms For Finite Dimensional Vector Spaces.
Author(s): Kenneth Kuttler
503 Pages
Linear Algebra A free Linear Algebra Textbook and Online Resource
This textbook is suitable for a sophomore level linear algebra course taught in about twenty-five lectures. It is designed both for engineering and science majors, but has enough abstraction to be useful for potential math majors. Our goal in writing it was to produce students who can perform computations with linear systems and also understand the concepts behind these computations.
Author(s): David Cherney, Tom Denton, Rohit Thomas and Andrew Waldron
436 Pages