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.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Covers the fundamentals of connectivity in graph theory, including paths, cycles, and spanning trees.

Explores finding the equation of the tangent line to a function's graph at a point.

Explores counterfactuals in SEMs and D-Separation in graphical models.

Explains the relationship between two functions through restriction and prolongation, emphasizing the importance of defining a function by its graph.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Covers the Stochastic Block Model for community detection, focusing on the detection of communities, clusters, and groups.

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

Explores real functions of two real variables, illustrating domains, images, and graphs.

Discusses the graph of a relation and the concept of separation.