K-means Algorithm

This lecture covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function. The instructor explains the process step by step, from initializing k centers at random to updating the centers iteratively. The lecture also touches on the expectation maximization-like approach and the challenges of computationally hard problems in clustering.