K-Means: Implementation and Analysis

This lecture focuses on the implementation and analysis of the K-Means algorithm. The instructor guides the students through generating Gaussian clusters, plotting them in a two-dimensional space, and implementing K-Means from scratch. The lecture covers how to assign points to clusters based on distances, compute the cost function, and update centroids iteratively. The instructor explains the importance of clever centroid initialization and demonstrates how noise affects the algorithm's performance. Additionally, the lecture showcases the comparison between the algorithm's computed centroids and the true means as noise levels increase, highlighting the algorithm's limitations in noisy data.