Mutual Information is a measure from information theory that quantifies the amount of information obtained about one random variable through another random variable. It helps to understand the relationship between two variables by indicating how much knowing one of them reduces uncertainty about the other. In mathematical terms, Mutual Information is defined as the difference between the entropy of a variable and the conditional entropy of that variable given another. It is commonly used in various fields, including machine learning, statistics, and data science, to assess the dependency between variables and improve model performance.