Principal Component Analysis: Dimensionality Reduction

This lecture covers the application of Principal Component Analysis (PCA) for dimensionality reduction in machine learning. The instructor demonstrates how PCA can reduce redundant information in joint angle trajectories, project high-dimensional state spaces onto lower dimensions, and preprocess data for classification and compression. PCA is shown to extract features and reduce computational costs, making it a valuable tool for data storage, retrieval, and classification tasks.