Lectures related to Linear applications and eigenvalues

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

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

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

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

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

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores vector spaces, linear transformations, matrices, eigenvalues, inner products, and operators.

Covers the reduction of non-diagonalizable matrices and their geometric interpretations.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

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

Covers the properties and operations of vector spaces, including addition and scalar multiplication.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

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

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

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Explains orthogonal families, bases, and projections in vector spaces.

Introduces orthogonal sets and bases, discussing their properties and linear independence.

Covers the concept of 3D vector space, scalar product, bases, orthogonality, and projections.

Covers eigenvalues, eigenvectors, and diagonalization of matrices.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.