Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.