Multivariate Methods II: ICA for Functional Brain Imaging

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Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

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

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Covers the basics of PCA, exercises on dimensionality reduction, and criteria for choosing projections.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.