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Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.

Explores optimal linear response for stochastic dynamical systems, addressing perturbations and mixing rate optimization.

Explores the Rank Theorem in linear algebra, covering matrix properties and determinants.

Introduces the kernel of a linear application and its properties.

Covers the basics of linear mapping and coordinate systems.

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Covers linear systems, vector subspaces, and the kernel and image of linear applications.