Linear Transformations: Matrices and Kernels

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

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Explores vector space operations, linear transformations, matrix representation, and linear applications.

Covers the analysis of functions, composition, and mathematical induction.

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

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

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

Explores real functions, their graphs, properties, and transformations, including symmetry and surjection.

Covers translations, homotheties, and geometric transformations using vectors and matrices.

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.