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.
Explores memory organization, virtualization, dynamic memory allocation, stack, heap, and memory virtualization techniques like base register and segmentation.
