Lectures related to Mean absolute error

Covers the conjugate gradient method, stopping criteria, and convergence properties in iterative optimization.

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Explores linear regression through least squares and normal equations, emphasizing the importance of minimizing errors for accurate predictions.

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Explains Lagrange multipliers for extremum points and multiple integration methods for volume and area calculations.