Gaussian Moxture Model GMM by Dr. B D Y Sunil

Gaussian Moxture Model GMM by Dr. B D Y Sunil | IARE | #GaussianMixtureModel #GMM #MachineLearning #ArtificialIntelligence #Clustering #UnsupervisedLearning #EMAlgorithm #DataMining #PatternRecognition Description: A Gaussian Mixture Model (GMM) is a probabilistic model used to represent a dataset as a combination of multiple Gaussian (normal) distributions. Each Gaussian component represents a cluster within the data, allowing GMMs to model complex and overlapping data distributions more effectively than traditional clustering methods. GMMs are widely used in machine learning, pattern recognition, computer vision, speech processing, and data analysis. The Expectation-Maximization (EM) Algorithm is commonly employed to estimate the parameters of a GMM. Website Link :- https://www.iare.ac.in/ Akanksha Link :- https://akanksha.iare.ac.in/ YouTubeLink :-    / @iarehyderabad   Facebook Link :-   / iareofficial   Instagram Link :-   / iare_hyderabad   Flickr Link :- flickr.com/photos/186282793@N05/albums #marrirajasekharreddy Instagram Link :-   / marrirajasekar   Facebook Link :-   / trsrajasekhar   Twitter :- https://x.com/marrirajasekar LinkedIn :-   / marri-rajasekhar-reddy-69a06844   Institute of Aeronautical Engineering Dundigal, Hyderabad – 500 043, Telangana, India. Phone:91546 78975, 91546 78976, 040-29705852, 29705853, 29705854 Email: [email protected], [email protected] Gaussian Mixture Model, GMM, Machine Learning, Clustering, Probability Distribution, Gaussian Distribution, Normal Distribution, Expectation-Maximization, EM Algorithm #GaussianMixtureModel #GMM #MachineLearning #ArtificialIntelligence #Clustering #UnsupervisedLearning #EMAlgorithm #DataMining #PatternRecognition