Explores design solution definition, multidisciplinary optimization, and challenges in system design optimization, including the roots and motivation behind Multidisciplinary Design Optimization.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.