Maximum Likelihood Estimation (MLE) in Supervised Learning

Study Notes

Maximum Likelihood Estimation (MLE)

The goal of MLE is to find the parameters that make the observed data most likely.

The Likelihood Function in MLE

The likelihood function represents the probability of observing the data given a set of model parameters.

MLE Process

After defining the likelihood function, the next step is to maximize it to find the maximum likelihood estimate (MLE) of the parameters.
The MLE process involves finding the values of the parameters that maximize the likelihood function.

MLE in Supervised Learning

MLE is considered a powerful technique in supervised learning because it allows for the estimation of model parameters from observed data, enabling accurate predictions.

Errors in Machine Learning

Bias and variance are related to reducible errors in machine learning, which can be minimized by adjusting the model's complexity.
The purpose of errors in machine learning is to measure the difference between the model's predictions and the true labels, guiding model improvement.

Reducible Errors in Machine Learning

Reducible errors can be minimized by improving the model or collecting more data.

Inverse Gaussian Distribution

A key property of the inverse Gaussian distribution, compared to the Gaussian distribution, is its asymmetry.
The inverse Gaussian distribution is skewed, with a longer tail on the right side, making it useful for modeling data with skewed distributions, such as survival times or financial returns.
The inverse Gaussian distribution is useful for modeling certain types of data, such as those with non-normal or skewed distributions.

Description

Test your knowledge of Maximum Likelihood Estimation (MLE), a method used in supervised learning to estimate model parameters that best explain observed data. Understand the goal of MLE and its common applications in estimating parameters of probabilistic models such as Gaussian distribution or logistic regression.