QR Factorization: Least Squares System Resolution

This lecture covers the QR factorization method applied to solving a system of linear equations in the least squares sense. It explains how for a matrix A with linearly independent columns, the equation AX = b has a unique least squares solution given by X = R^-1 * Q^T * b. The lecture also discusses the properties of the matrix Q, the uniqueness of the solution, and the orthogonal projection in the space of column vectors of Q.