Principal Component Analysis: Properties and Applications

Related lectures (31)

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Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

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