本书内容包括行列式、矩阵、线性方程组与向量、矩阵的特征值与特征向量、二次型及Mathematica软件的应用等。每章都配有习题，书后给出了习题答案。本书在编写中力求重点突出、由浅入深、通俗易懂，努力体现教学的适用性。本书可作为高等院校工科专业的学生的教材，也可作为其他非数学类本科专业学生的教材或教学参考书。

1 Introduction to Vectors
1.1 Vectors and Linear Combinations
1.2 Lengths and Dot Products
1.3 Matrices
2 Solving Linear Equations
2.1 Vectors and Linear Equations
2.2 The Idea of Elimination
2.3 Elimination Using Matrices
2.4 Rules for Matrix Operations
2.5 Inverse Matrices
2.6 Elimination = Factorization: A = LU
2.7 Transposes and Permutations
3 Vector Spaces and Subspaces
3.1 Spaces of Vectors
3.2 The Nullspace of A: Solving Ax = 0and Rx =0
3.3 The Complete Solution to Ax = b
3.4 Independence,Basis and Dimension
3.5 Dimensions of the Four Subspaces
4 Orthogonality
4.1 Orthogonality of the Four Subspaces
4.2 Projections
4.3 Least Squares Approximations
4.4 Orthonormal Bases and Gram-Schmidt
5 Determinants
5.1 The Properties of Determinants
5.2 Permutations and Cofactors
5.3 Cramer’s Rule,Inverses,and Volumes
6 Eigenvalues and Eigenvectors
6.1 Introduction to Eigenvalues
6.2 Diagonalizing a Matrix
6.3 Systems of Differential Equations
6.4 Symmetric Matrices
6.5 Positive Definite Matrices
7 The Singular Value Decomposition (SVD)
7.1 Image Processing by Linear Algebra
7.2 Basesand Matricesin the SVD
7.3 Principal Component Analysis (PCA by the SVD)
7.4 The Geometry of the SVD
8 Linear Transformations
8.1 The Idea of a Linear Transformation
8.2 The Matrix of a Linear Transformation
8.3 The Search for a Good Basis
9 Complex Vectors and Matrices
9.1 Complex Numbers
9.2 Hermitian and Unitary Matrices
9.3 The Fast Fourier Transform
10 Applications
10.1 Graphs and Networks
10.2 Matrices in Engineering
10.3 Markov Matrices, Population, and Economics
10.4 Linear Programming
10.5 Fourier Series: Linear Algebra for Functions
10.6 Computer Graphics
10.7 Linear Algebra for Cryptography
11 Numerical Linear Algebra
11.1 Gaussian Eliminationin Practice
11.2 Norms and Condition Numbers
11.3 Iterative Methods and Preconditioners
12 Linear Algebra in Probability& Statistics
12.1 Mean,Variance,and Probability
12.2 Covariance Matrices and Joint Probabilities
12.3 Multivariate Gaussian and Weighted Least Squares
Matrix Factorizations
Index
Six Great Theorems/Linear Algebra in a Nutshell