Maximum Likelihood Estimate Lecture
In this lecture, we will introduce Maximum Likelihood Estimation (MLE), a density estimation method in machine learning. We will discuss what learning problems can be solved using Maximum Likelihood Estimation. In addition, we will derive the Maximum Likelihood Estimate of three very popular probability distributions: Bernoulli distribution, univariate Gaussian distribution and multivariate Gaussian distribution. We will also touch on interesting related concepts and ideas such as Central Limit theorem, the Bias-Variance dilemma and Hoeffding’s inequality.
Download the slides here.