Vector Spaces: Operations and Linear Transformations

Related lectures (26)

Page 1 of 3

Covers matrices, linear applications, vector spaces, and bijective functions.

Introduces vector definitions, displacement, addition, and applications in geometry.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Covers translations, homotheties, and geometric transformations using vectors and matrices.

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

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Introduces abstract concepts in linear algebra, focusing on operations with vectors and matrices.

Covers the reduction of an application, finding reduced forms of matrices, diagonalizability, and polynomial roots.

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

Covers the definition and properties of vector spaces, along with examples like Euclidean spaces and matrix spaces.