K-Means Algorithm: Implementation and Application as Recommendation System

This lecture covers the practical implementation of the K-Means algorithm in Matlab, focusing on coding the algorithm, evaluating its performance on high-dimensional datasets, and creating a recommendation system. Topics include centroid initialization methods, distance calculation, assignment and update steps, convergence checking, and metrics computation. Additionally, it explores the application of K-Means as an unsupervised recommendation system for constructing Magic the Gathering decks using top player data.