Lectures related to Change of Frames in 2D

Explores the properties and applications of matrix transpose, including rotations and inverse matrices.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers matrix multiplication, properties, and inverses in linear algebra.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.

Explains the inertia tensor, main axes, principal moments, and balancing of rotating solids.

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.

Explores injective and surjective linear transformations, kernel, image, and matrix operations.

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Explores matrices, inverses, and their applications in linear algebra, emphasizing composition and transformation properties.

Introduces key concepts in linear algebra, including matrices, operations, and numerical invariants.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.