Lectures related to Multilinear subspace learning

Orthogonality and Subspaces

Explores orthogonality, vector norms, and subspaces in Euclidean space, including determining orthogonal complements and properties of subspaces and matrices.

Quadratic Best Approximation

Explores the best quadratic approximation in Euclidean spaces, emphasizing least squares.

Injective Morphisms in Mathematics

Introduces injective morphisms and vector spaces with examples and verification methods.

Neural Network Approximation and Learning

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Vector Spaces: Examples and Subspaces

Covers examples of vector spaces and the concept of subspaces, emphasizing key properties and verification methods.

Orthogonal Bases in Vector Spaces

Covers the concept of orthogonal bases in vector spaces and Pythagorean theorem applications.

Matrix Operations: LU Factorization & Subspace Vector

Covers LU factorization, determinants, subspaces, and matrix products.