Lectures related to Oja's Rule

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.

Explores algebraic and geometric multiplicities of eigenvalues in linear algebra.

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

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

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.

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

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

Explores PCA for predicting medals distribution and compressing face images.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.