Unsupervised Learning: Clustering & Dimensionality Reduction

This lecture covers the fundamentals of unsupervised learning, focusing on clustering with K-means and dimensionality reduction using Principal Component Analysis (PCA). It explains how unsupervised learning identifies patterns in data without predefined labels. The instructor discusses the K-means algorithm for grouping data points based on proximity and PCA for finding a new set of features that best represent the data. The lecture also introduces autoencoders as neural networks for dimensionality reduction. Practical examples and applications are provided to illustrate the concepts.