Lectures related to Untitled | EPFL Graph Search

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.

Explains the Gaussian elimination method for transforming matrices into reduced row-echelon form using elementary row operations.

Covers the concepts of kernel and image of a linear transformation and their relationship with the rank of the matrix.

Covers the determination of matrices associated with linear transformations and explores the kernel and image concepts.

Explores the concept of variety defined as the closure of VCA and its applications in algebraic geometry.

Covers linear transformations using matrices, focusing on linearity, image, and kernel.

Explores injective and surjective linear transformations, kernel, image, and matrix operations.

Explains row-echelon and reduced row-echelon matrices and their role in simplifying system resolution.

Explores forming bases, dependencies, and relationships in linear applications using invertible matrices and canonical bases.

Delves into the Rank Theorem's implications for linear transformations and mappings.