Lectures related to Isogeny Graphs: Eigenvalues and Cryptography

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

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

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

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Covers the method of diagonalization for determining if a non-square matrix A is diagonalizable.

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

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

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

Explores the application of matrices and eigendecompositions in networks.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

Covers the concept of group cohomology, focusing on chain complexes, cochain complexes, cup products, and group rings.

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

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

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Explores the Laplacian matrix and consensus in networked control systems.

Explores diagonalization conditions, bases by eigenvectors, and generalization of concepts.