PCA: Interactive class

Related lectures (31)

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

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

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

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

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

Explores Spectral Clustering, eigenvalue decomposition, Laplacian matrices, and cluster identification through eigenvector projections.

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