Counterfactuals: SEM and D-Separation

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

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

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

Covers variational inference and neural networks for classification tasks.

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

Explores coordination in networked control systems, graph theory, and consensus algorithms.

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

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

Covers the generalities of functions, including the definition of an application between sets and the uniqueness of elements in the image set.

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.