Expectation Maximization Algorithm in Machine Learning | EM Algorithm in Machine Learning

Expectation Maximization Algorithm | EM Algorithm in Machine Learning | Expectation Maximization Algorithm in Machine Learning | EM Algorithm | em algorithm in machine learning in Hindi | em algorithm in Hindi | em algorithm in machine learning | aktu machine learning

The algorithm aims to find the maximum likelihood (ML) or maximum a posteriori (MAP) estimates of the parameters in the presence of latent (hidden) variables. It alternates between two main steps: the E-step (Expectation step) and the M-step (Maximization step). Each iteration of the algorithm seeks to improve the parameter estimates until convergence is achieved.

Here's a step-by-step explanation of the EM algorithm:

Step 1: Initialization: Start with initial guesses for the model parameters. These initial values can be random or based on prior knowledge.

Step 2: E-step (Expectation step): In this step, the algorithm computes the expected values of the latent variables given the observed data and the current parameter estimates. It calculates the posterior distribution of the latent variables.

Step 3: M-step (Maximization step): Using the expected values of the latent variables from the E-step, the algorithm updates the model parameters to maximize the expected complete log-likelihood of the observed data.

Step 4: Convergence Check: Check for convergence. If the change in the estimated parameters is below a predefined threshold or the log-likelihood converges, stop; otherwise, go back to Step 2.

Step 5: Output: The algorithm outputs the estimated model parameters that provide the maximum likelihood or maximum posterior estimates given the incomplete data.

The EM algorithm is widely used in various statistical and machine learning applications, such as clustering (e.g., Gaussian Mixture Models), hidden Markov models, and missing data imputation.

It's important to note that EM can be sensitive to the choice of initial parameter values, and in some cases, it might not converge to the global maximum of the likelihood. In such cases, multiple restarts with different initializations or other optimization techniques may be employed.

The EM algorithm provides a powerful tool for handling missing data and estimating parameters in models with hidden variables, contributing to the broader fields of statistical modeling and machine learning.
EM Algorithm in Machine Learning in Hindi | Expectation Maximization Algorithm | EM Algorithm | em algorithm in Hindi | Expectation Maximization Algorithm in Machine Learning | em algorithm in machine learning
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EM Algorithm in Machine Learning | Expectation Maximization Algorithm | EM Algorithm | em algorithm in Hindi | Expectation Maximization Algorithm in Machine Learning | em algorithm in machine learning

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EM Algorithm in Machine Learning in Hindi | Expectation Maximization Algorithm | EM Algorithm | em algorithm in Hindi

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EM Algorithm in Machine Learning | Expectation Maximization Algorithm | EM Algorithm | em algorithm in Hindi | Expectation Maximization Algorithm in Machine Learning | em algorithm in machine learning

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