Estimating the Term Structure: Principal Component Analysis

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

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

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

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Covers Oja's rule in Neurorobotics, focusing on learning eigenvectors and eigenvalues for capturing maximal variance.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

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.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.