Expectation-Maximization (EM) Algorithm by Dr. B D Y Sunil

Expectation-Maximization (EM) Algorithm by Dr. B D Y Sunil | IARE | #ExpectationMaximization #EMAlgorithm #MachineLearning #ArtificialIntelligence #DataMining #StatisticalLearning #PatternRecognition Description: The Expectation-Maximization (EM) Algorithm is an iterative statistical technique used to estimate unknown parameters in probabilistic models when data contains missing values or hidden (latent) variables. The algorithm alternates between the Expectation (E) step, which estimates the hidden variables, and the Maximization (M) step, which updates the model parameters to maximize the likelihood of the observed data. EM is widely used in machine learning, data mining, clustering, pattern recognition, and computer vision applications. 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] Expectation-Maximization, EM Algorithm, Machine Learning, Statistical Learning, Latent Variables, Hidden Variables, Maximum Likelihood Estimation, Probabilistic Models #ExpectationMaximization #EMAlgorithm #MachineLearning #ArtificialIntelligence #DataMining #StatisticalLearning #PatternRecognition #GaussianMixtureModel #Clustering #MaximumLikelihood #ProbabilisticModels #DataScience #ComputerVision #DeepLearning