Multivariable Control: Eigenvalues and Modes

Related lectures (28)

Page 2 of 3

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

Explores exact and approximate sampling in multivariable control systems, discussing stability, eigenvalues, and system properties.

Covers the concept of diagonalization of matrices through the study of eigenvectors and eigenvalues.

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

Explores eigenvalues, eigenvectors, and matrix diagonalization with examples and proofs.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

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

Explores reachability and controllability in multivariable control systems, discussing tests, proofs, and their implications.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Covers the Power Method for approximating the largest eigenvalue of a matrix.