Maximum Likelihood Estimation (MLE)

A statistical method for estimating population parameters (e.g., mean, variance) by selecting values that maximize the likelihood function, making the observed data most probable. It is widely used in machine learning for training models, such as logistic regression or neural networks, by finding parameters that best fit the data.

How It Works

1. Assume a distribution: Assume the data follows a specific probability distribution with unknown parameters ($\theta$).
2. Define the Likelihood Function: Create a function $L(\theta )$ that represents the probability of observing the given data, usually as a product of individual probabilities (or likelihoods) for each data point.
3. Maximize the Likelihood: Find the parameter value ($\hat{\theta }$) that maximizes $L(\theta )$, often by taking the derivative of the log-likelihood and setting it to zero.