Glossary for Linear Algebra + Quick Tips

In summary: W. Gilbert Strang on Linear Algebra:In summary, Gilbert Strang's lectures on Linear Algebra are available on-line at ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/. The lectures are well-made and provide a good introduction to the subject.
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Author: Gilbert Strang from MIT
 

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Thanks Greg! That couldn't have come at a better time for me!
 
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Thank you, for this subject
 
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And in case you haven't checked it out already, there's also the accompanying http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/CourseHome/index.htm". The video lectures on the page are pretty good, although you may want to download the full ~100 mb file to reduce stuttering if your connection is not up to it.
 
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W. Gilbert Strang's lectures on Linear Algebra are available on-line

http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/

Lecture 1 - The Geometry of Linear Equations
Lecture 2 - Elimination with Matrices
Lecture 3 - Multiplication and Inverse Matrices
Lecture 4 - Factorization into A = LU
Lecture 5 - Transposes, Permutations, Spaces Rn
Lecture 6 - Column Space and Nullspace
Lecture 7 - Solving Ax = 0: Pivot Variables, Special Solutions
Lecture 8 - Solving Ax = b: Row Reduced Form R
Lecture 9 - Independence, Basis, and Dimension
Lecture 10 - The Four Fundamental Subspaces
Lecture 11 - Matrix Spaces; Rank 1; Small World Graphs
Lecture 12 - Graphs, Networks, Incidence Matrices
Lecture 13 - Quiz 1 Review
Lecture 14 - Orthogonal Vectors and Subspaces
Lecture 15 - Projections onto Subspaces
Lecture 16 - Projection Matrices and Least Squares
Lecture 17 - Orthogonal Matrices and Gram-Schmidt
Lecture 18 - Properties of Determinants
Lecture 19 - Determinant Formulas and Cofactors
Lecture 20 - Cramer's Rule, Inverse Matrix, and Volume
Lecture 21 - Eigenvalues and Eigenvectors
Lecture 22 - Diagonalization and Powers of A
Lecture 23 - Differential Equations and exp(At)
Lecture 24 - Markov Matrices; Fourier Series
Lecture 24b - Quiz 2 Review
Lecture 25 - Symmetric Matrices and Positive Definiteness
Lecture 26 - Symmetric Matrices and Positive Definiteness
Lecture 27 - Positive Definite Matrices and Minima
Lecture 28 - Similar Matrices and Jordan Form
Lecture 29 - Singular Value Decomposition
Lecture 30 - Linear Transformations and Their Matrices
Lecture 31 - Change of Basis; Image Compression
Lecture 32 - Quiz 3 Review
Lecture 33 - Left and Right Inverses; Pseudoinverse
Lecture 34 - Final Course Review

Also on Youtube - starting with Lecture 1 (Spring 2005) -
 
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1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their relationships with vectors and matrices. It is used to solve problems in physics, engineering, economics, and many other fields.

2. What is a vector?

A vector is a mathematical object that has both magnitude and direction. It is represented by an arrow, with the length of the arrow representing the magnitude and the direction of the arrow representing the direction.

3. What is a matrix?

A matrix is a rectangular array of numbers or other mathematical objects. It is often used to represent systems of equations or to perform operations on vectors.

4. What are the basic operations in linear algebra?

The basic operations in linear algebra include addition, subtraction, multiplication, and division. These operations can be performed on vectors and matrices to solve equations and manipulate data.

5. How is linear algebra used in real life?

Linear algebra is used in a variety of real-life applications, such as computer graphics, data analysis, machine learning, and optimization problems. It is also used in physics and engineering to model and solve problems.

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