Principal Component Analysis: Olympic Medals & Image Compression

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Page 3 of 4

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

Covers K-means algorithm for image compression and PCA for dimensionality reduction.

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

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

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

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

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

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

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

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