Nonlinear dimensionality reduction

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Dimensionality reduction

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Related lectures (32)

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Unsupervised Learning: Dimensionality Reduction

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

Spectral Clustering: Theory and Applications

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

Dimensionality Reduction

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

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Linear Dimensionality Reduction

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

Dimensionality Reduction

Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

PCA: Interactive class

On PCA includes interactive exercises and emphasizes minimizing information loss.

PCA: Directions of Largest Variance

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