Linear Transformations: Matrices and Kernels

Related lectures (29)

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

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

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

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Covers the geometry of the triangle, including inequalities, heights, and centers.

Explores the scalar product of vectors, including properties and calculation methods.

Covers landmarks, coordinate systems, frames, and terminology in coordinates, emphasizing geometric angles and orthogonal vectors.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Covers the matrix representations of linear applications and the equivalence between matrices.