Linear Algebra: Normal Equations and Symmetric Matrices

Related lectures (28)

Page 1 of 3

Covers the organization of linear algebra course and exercises for civil engineering and environmental sciences students.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.