Pseudorandomness: Theory and Applications

Related lectures (29)

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

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

Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Explores automorphism groups in trees and graphs, focusing on ends and types of automorphisms.

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

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Introduces differential forms on manifolds, covering tangent bundles and intersection pairings.

Delves into optimization on manifolds, showcasing simple applications like finding largest eigenvalues and singular values.

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