Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Discusses algorithms for balancing exploration and exploitation in decision-making processes.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Introduces linear algebra, calculus, and optimization basics in Euclidean spaces, emphasizing the power of optimization as a modeling tool.
