Information Theory: Basics

Related lectures (29)

Page 2 of 3

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

Explores graph coloring, directed cycles, LLL algorithm applications, and element dependencies in graphs.

Covers graphical models for probability distributions and factor graphs representation.

Introduces graph theory basics, Ramsey theory, and graph coloring concepts.

Covers the basics and applications of graph coloring, including balancing vectors and achieving perfect fairness.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explains entropy and free energy computation in graph coloring.

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

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

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.