Covers the basics of algorithms, their historical significance, pseudo-code representation, and practical tasks in computational thinking.
Explores the relationship between matchings, entropy, and Bethe free entropy in random graphs.
Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.
Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.
Introduces the Girvan-Newman Algorithm for network decomposition based on edge betweenness and demonstrates its application in physics collaborations.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
