Covers the fundamentals and stability analysis of Networked Control Systems, including software installation, dynamical systems, equilibrium states, and stability testing.
Explores the theory and applications of convex optimization, covering topics such as log-determinant function, affine transformations, and relative entropy.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.