Linear Applications: Kernel and Preimage (Part 2)

Related lectures (30)

Page 2 of 3

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

Covers the calculation of dimensions in linear algebra, focusing on determining the dimension of the kernel of a given matrix.

Delves into the Rank Theorem's implications for linear transformations and mappings.

Covers the concepts of image and kernel in linear algebra, focusing on uniqueness and orthogonal projections.

Covers the basics of linear algebra, including linear maps, bases, and matrix operations.

Explores subspaces, matrix equations, linear transformations, and their matrix representations in linear algebra.

Covers linear systems, vector subspaces, and the kernel and image of linear applications.

Explains the Rank Theorem for linear applications in 3D space and its implications.

Explores linear transformations in 3D space using matrices and their applications.

Covers the definition of linear applications in IR³ to R³ and explores examples and properties.