Edge coloring

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Covers the topology of Riemann surfaces and the concept of triangulation using finitely many triangles.

Covers statistical physics tools for optimization, learning, graph coloring, recommendation systems, and neural networks.

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Introduces unweighted bipartite matching and its solution using linear programming and the simplex method.

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

Introduces graph sketching, generalizations like multigraphs, and weighted graphs.

Covers the computation of Bethe free entropy and the interpretation of messages between variables and factors.

Explores magnetic fields, self-adjusting groups, and particle suspension in practical applications.