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Lecture
Principal Component Analysis: Dimension Reduction
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Dimensionality Reduction: PCA & LDA
Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Image Classification: Decision Trees & Random Forests
Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
