Lectures related to Rank–nullity theorem

Explores linear applications, matrices, ranks, kernels, and dimensions in linear algebra.

Explores Gaussian linear regression, design matrix, least squares estimation, and geometric interpretation in linear regression analysis.

Explores changing bases in vector spaces and matrix representation of linear transformations.

Covers vector spaces, subspaces, kernel, image, linear independence, and bases in linear algebra.

Covers 3x3 matrix representations and the concept of rank in linear algebra.

Covers bijective linear maps, invertibility of matrices, isomorphisms, and the rank theorem.

Explores matrix rank, subspaces, and their role in solving linear systems of equations.

Covers the definition of linear applications and their properties, including examples and matrix representation.