L22: Gaussian mixture models | latent variables, generative story & multimodal data

Welcome to Lecture 30 of the course "Machine Learning Techniques" by Prof. Arun Rajkumar. Full Course: https://study.iitm.ac.in/ds/course_pa... Video Overview Is your data not well-represented by a single Gaussian distribution? This lecture introduces Gaussian Mixture Models (GMMs), a powerful tool for unsupervised learning and estimation when dealing with multimodal data. We explore why simple Gaussians often fail to capture the complexity of real-world datasets and how GMMs provide a richer, more flexible modeling framework. The lecture introduces the two-step generative process behind GMMs, explains the role of latent variables, and sets the stage for parameter estimation using maximum likelihood methods. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/stud... #UnsupervisedLearning #GaussianMixtureModel #GMM #Estimation #MaximumLikelihood #LatentVariables #DataModeling #MachineLearning #Clustering #ProbabilisticModeling #MixtureOfGaussians #MixtureModels #GenerativeModels #MultimodalData #DataScience #Statistics #AppliedML #MachineLearningLecture #DataAnalysis