Covers the Conjugate Gradients method for solving linear systems iteratively with quadratic convergence and emphasizes the importance of linear independence among conjugate directions.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.
