Lecture
This lecture covers the concepts of dimensionality reduction, focusing on Principal Component Analysis (PCA), and discusses the objectives and methods of PCA. It also introduces clustering techniques like K-means clustering and Mean Shift, along with density estimation methods such as Kernel Density Estimation and Gaussian Mixture Models.