Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Explores the Szemerédi Regularity Lemma, e-regularity in bipartite graphs, supergraph structure, and induction techniques.
Covers polynomial identity testing using oracles and random point evaluation, with applications in graph theory and algorithmic aspects.
Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.
Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.
