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PCA and Kernel PCA
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Related lectures (32)
Dimensionality Reduction: PCA & t-SNE
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Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Data Representation: PCA
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Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Principal Component Analysis: Eigenfaces
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Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.
Dimensionality Reduction
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Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.
PCA: Interactive class
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On PCA includes interactive exercises and emphasizes minimizing information loss.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Unsupervised Learning: Clustering & Dimensionality Reduction
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Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Dimensionality Reduction: PCA and LDA
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Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.
PCA: Key Concepts
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Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Principal Component Analysis: Geometric Interpretation and Dimension Reduction
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Explores Principal Component Analysis for dimension reduction and data representation in a new basis.
Feature Extraction & Clustering Methods
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Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Dimension Reduction: Curse of Dimensionality
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Explores dimension reduction and the curse of dimensionality, highlighting the exponential relation between examples and dimension.
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