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MIT 18.06 Linear Algebra Spring 2005

MIT 18.06 Linear Algebra Spring 2005

Course Highlights

This course features a complete set of video lectures by Professor Gilbert Strang. There are also Java applets with sound as well as interactive Java applets. The text for this class is Introduction to Linear Algebra by Gilbert Strang.

Course Description

This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.

Course Homepage 18.06 Linear Algebra Spring 2005

Course features at MIT OpenCourseWare page: *Syllabus *Calendar *Readings *Assignment *Exams *Tools *Study Materials *Related Resources *Download Course Materials

Complete MIT OCW video collection at MIT OpenCourseWare - VideoLectures.NET

Unit I: Ax = b and the Four Subspaces

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Graphs and Networks

Nikola Kamburov

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09:03

Solving Ax=b

Martina Balagovic

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09:34

LU Decomposition

Benjamin Harris

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14:45

Subspaces of Three Dimensional Space

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10:17

Elimination with Matrices

Martina Balagovic

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14:52

Exam 1 Problem Solving

Nikola Kamburov

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10:44

Computing the Four Fundamental Subspaces

Benjamin Harris

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08:10

Basis and Dimension

Ana Rita Pires

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10:03

Solving Ax=0

Martina Balagovic

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08:24

Inverse Matrices

Ana Rita Pires

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16:35

Geometry of Linear Algebra

Linan Chen

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08:55

Matrix Spaces

Ana Rita Pires

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08:45

Vector Subspaces

Nikola Kamburov

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07:43

An Overview of Key Ideas

Martina Balagovic

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Unit II: Least Squares, Determinants and Eigenvalues

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09:22

Eigenvalues and Eigenvectors

Nikola Kamburov

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13:49

Determinants and Volume

Linan Chen

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09:55

Properties of Determinants

Ana Rita Pires

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10:20

Orthogonal Vectors and Subspaces

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11:48

Markov Matrices

David Shirokoff

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18:40

Differential Equations and exp(At)

Linan Chen

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08:03

Least Squares Approximation

Benjamin Harris

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17:51

Exam 2 Problem Solving

Linan Chen

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12:48

Determinants

Linan Chen

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09:07

Powers of a Matrix

Benjamin Harris

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10:00

Gram-Schmidt Orthogonalization

Ana Rita Pires

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09:50

Projection onto Subspaces

Nikola Kamburov

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Unit III: Positive Definite Matrices and Applications

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13:01

Complex Matrices

David Shirokoff

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14:39

Pseudoinverses

David Shirokoff

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12:25

Change of Basis

Martina Balagovic

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08:12

Similar Matrices

Benjamin Harris

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12:49

Exam 3 Problem Solving

David Shirokoff

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10:46

Linear Transformations

Nikola Kamburov

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12:50

Positive Definite Matrices and Minima

Martina Balagovic

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11:36

Computing the Singular Value Decomposition

Benjamin Harris

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Final Course Review

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Final Exam Problem Solving

Ana Rita Pires

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Lecture 19: Determinant Formulas and Cofactors

Gilbert Strang

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43:51

Lecture 25: Symmetric Matrices and Positive Definiteness

Gilbert Strang

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Lecture 29: Singular Value Decomposition

Gilbert Strang

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49:24

Lecture 17: Orthogonal Matrices and Gram-Schmidt

Gilbert Strang

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49:19

Lecture 10: The Four Fundamental Subspaces

Gilbert Strang

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43:25

Lecture 34: Final Course Review

Gilbert Strang

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48:04

Lecture 16: Projection Matrices and Least Squares

Gilbert Strang

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Lecture 20: Cramer's Rule, Inverse Matrix, and Volume

Gilbert Strang

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Lecture 2: Elimination with Matrices

Gilbert Strang

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Lecture 28: Similar Matrices and Jordan Form

Gilbert Strang

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Lecture 11: Matrix Spaces; Rank 1; Small World Graphs

Gilbert Strang

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Lecture 15: Projections onto Subspaces

Gilbert Strang

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Lecture 21: Eigenvalues and Eigenvectors

Gilbert Strang

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Lecture 6: Column Space and Nullspace

Gilbert Strang

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Lecture 23: Differential Equations and exp(At)

Gilbert Strang

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Lecture 13: Quiz 1 Review

Gilbert Strang

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Lecture 8: Solving Ax = b: Row Reduced Form R

Gilbert Strang

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Lecture 9: Independence, Basis, and Dimension

Gilbert Strang

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Lecture 1: The Geometry of Linear Equations

Gilbert Strang

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Lecture 4: Factorization into A = LU

Gilbert Strang

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Lecture 3: Multiplication and Inverse Matrices

Gilbert Strang

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Lecture 5: Transposes, Permutations, Spaces R^n

Gilbert Strang

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Lecture 27: Positive Definite Matrices and Minima

Gilbert Strang

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Lecture 22: Diagonalization and Powers of A

Gilbert Strang

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Lecture 24b: Quiz 2 Review

Gilbert Strang

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Lecture 32: Quiz 3 Review

Gilbert Strang

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Lecture 26: Complex Matrices; Fast Fourier Transform

Gilbert Strang

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Lecture 14: Orthogonal Vectors and Subspaces

Gilbert Strang

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Lecture 18: Properties of Determinants

Gilbert Strang

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Lecture 31: Change of Basis; Image Compression

Gilbert Strang

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Lecture 24: Markov Matrices; Fourier Series

Gilbert Strang

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Lecture 7: Solving Ax = 0: Pivot Variables, Special Solutions

Gilbert Strang

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Lecture 30: Linear Transformations and Their Matrices

Gilbert Strang

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Lecture 12: Graphs, Networks, Incidence Matrices

Gilbert Strang

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Lecture 33: Left and Right Inverses; Pseudoinverse

Gilbert Strang

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