Lectures related to Exam Guidelines: COVID Measures 2021

Explores linear combinations of vectors and matrices in Rn, demonstrating geometric interpretations and matrix operations.

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.