Dimensionality Reduction

Related lectures (32)

Page 2 of 4

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

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

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

Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

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

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.