I delivered a colloquium talk at the Department of Physics at Inha University titled “Calculus, Information Theory, and Data Science.”
Abstract: In this presentation, I explore the trajectory from introductory calculus to the foundational concepts of information theory and data science. We examine uncertainty through entropy and discuss how Gibbs and Gaussian measures represent states of maximal uncertainty under specific constraints (e.g., energy). After covering these fundamental ideas, the talk delves into their applications in data science, with a special focus on Bayesian inference for complex dynamical systems.
This talk is geared toward junior and senior undergraduate as well as graduate students interested in physics, mathematics, and data science. It assumes familiarity with first-year calculus and basic high school probability.