Unsupervised Learning: Principal Component Analysis

Related lectures (28)

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Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

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

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

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

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

Covers the basics of linear algebra, including matrix operations and singular value decomposition.