Lectures related to Stochastic Block Model: Community Detection

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Covers matrices, linear applications, vector spaces, and bijective functions.

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

Covers the analysis of functions, composition, and mathematical induction.

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

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Explores the properties and applications of Cayley graphs in group theory and network analysis.