Lectures related to Oja's Rule

Covers the multivariate normal distribution, properties, and sampling methods.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Covers eigenvectors, eigenvalues, and their significance in linear applications.

Covers stable vectorial lines, eigenvectors, and their properties under linear transformations in R³.

Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.

Covers eigenvalues, eigenvectors, and their applications in linear algebra.