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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
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.
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