TA Session: K-means and Gaussian Mixture EM Practice

This repository contains code for the practice session of the KAIST Graduate School of AI - Samsung Electronics DS Non-Degree Program.

Overview

The code implements practical exercises for two important algorithms in unsupervised learning:

• K-means Clustering: A simple yet powerful clustering technique.
• Gaussian Mixture Model (GMM) with Expectation Maximization (EM): A probabilistic model used for clustering, relying on the EM algorithm for parameter estimation.

Requirements

• Python 3
• NumPy
• SciPy
• Matplotlib
• scikit-learn