Lectures related to Determinant, Area, and Volume

Covers elementary matrix operations and vector spaces, including properties and conditions for invertibility.

Covers the Gram-Schmidt algorithm for orthonormal bases in vector spaces.

Explores elementary matrices in linear algebra and their role in matrix transformations.

Covers matrix notation, linear equations, vector spaces, and applications of least squares.

Explores the definition and properties of vector subspaces in linear algebra.

Covers the concepts of kernel and image of a linear transformation and their relationship with the rank of the matrix.

Explores linear combinations, basis determination, and vector space dimensionality through practical examples and exercises.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.