Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
Explores Ant Colony Optimization (ACO) for routing and optimization, discussing constructive heuristics, local search, pheromone mechanisms, and real-world applications.
