Dimensionality Reduction: PCA and Kernel PCA

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Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

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

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

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