Canonical Correlation Analysis: Linear and Kernel CCA

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Page 3 of 4

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

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

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

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

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

Explores Kernel Ridge Regression, equivalent formulations, Representer Theorem, Kernel trick, and predicting with kernels.