Lectures related to Linear Mapping and Bases

Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Covers the concept of basis, linear transformations, matrices, inverses, determinants, and bijective transformations.

Explores subspaces in linear algebra and transformations, including kernels and images of linear transformations.

Covers determining vector spaces, calculating kernels and images, defining bases, and discussing subspaces and vector spaces.

Covers linear algebra basics, including matrices, change of basis, and invertible matrices.

Explains linear independence, basis, and matrix rank with examples and exercises.

Offers a quick review of key linear algebra concepts essential for further topics.

Covers linear transformations between polynomial spaces and explores examples of linear independence and bases.

Explores matrix operations, rank determination, kernel dimensions, and basis concepts in linear algebra.

Explores the concept of linear independence in vector spaces through definitions and illustrative examples.

Covers the basics of linear mapping and coordinate systems.

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

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

Covers linear independence, bases, and coordinate systems with examples and theorems.

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

Explains kernel, image, and linear maps, illustrating concepts with examples.

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

Covers the extraction of bases from families of vectors and the concept of coordinate application in vector spaces.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

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