Matrices and Networks

Related lectures (31)

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Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

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

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Explores the construction of Ramanujan graphs using polynomials and addresses challenges with the probabilistic method.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores isogenic graphs, spectral properties, and mathematical applications in modular forms and cryptography.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.