Graphs and matrices

Related lectures (30)

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

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

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

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Explores pseudo randomness in graphs using eigenvalues and polynomials, emphasizing the significance of bunched roots and common interlacers.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Explores the Matrix Tree Theorem and its application in calculating spanning trees in graphs.

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.