Expectation Maximization Algorithm | Gaussian Mixture Model | Explained with Example

About this lesson

📖 Notes:- https://robosathi.com/docs/machine_learning/unsupervised/gaussian_mixture_model/expectation-maximization/ 🎥 Next Video: Anomaly Detection :- https://youtu.be/gBP7z0Ty4Ic 👉 In this video, we build the Expectation Maximization (EM) algorithm from Gaussian Mixture Models using the classic chicken-and-egg problem. 🎯 Learning Objectives ✅ Understand why EM is needed for Gaussian Mixture Models ✅ Explain the chicken-and-egg problem in latent variable models ✅ Interpret hard vs soft cluster assignments ✅ Understand indicator variables and responsibilities intuitively ✅ Compute responsibilities using GMM densities ✅ Explain the Maximization step and parameter updates ✅ Visualize how EM converges over iterations 👉 Maths for ML Playlist: https://www.youtube.com/playlist?list=PLnpa6KP2ZQxePOg6k6vAkcg5Y50EAZds9 🕔 Time Stamp 🕘 00:00:00 - 00:00:24 Introduction 00:00:25 - 00:02:15 GMM as Latent Variable Revision 00:02:16 - 00:03:30 Chicken🐓 and Egg🥚 Problem 00:03:01 - 00:05:10 Gaussians Clusters 00:05:11 - 00:07:05 GMM Densities at x=2.5 00:07:06 - 00:08:30 Break the Loop 00:08:31 - 00:12:20 Hard to Soft Assignment 00:12:21 - 00:16:44 Indicator Variable → Responsibility 00:16:45 - 00:25:44 Example of GMM Densities & Responsibility 00:25:45 - 00:28:25 Expectation Maximization Algorithm 00:28:26 - 00:30:40 Expectation Step 00:30:41 - 00:33:40 Maximization Step 00:33:41 - 00:37:20 Effective Number of Points in Cluster 00:37:21 - 00:38:30 The Convergence Process 00:38:31 - 00:39:06 What's Next? 🤔 #ai #ml #gmm #latentvariable #expectations #log #likelihood

Original Description