Lectures related to Recommender Systems: Matrix Factorization & Evaluation

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers the factorization of a matrix and the least squares method.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Explores direct methods for solving linear equations and the impact of errors on solutions and matrix properties.

Covers direct methods for solving linear systems in numerical analysis.