Lectures related to Linear Algebra: Bases and Transformations

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

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

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

Explores the application of linear algebra in quantum mechanics, emphasizing vector spaces, Hilbert spaces, and the spectral theorem.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

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

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