Introduction to Nonparametric Bayesian Models

This course aims to introduce learners to Nonparametric Bayesian Models, focusing on the flexibility they offer compared to traditional supervised machine learning techniques. The course covers topics such as Parametric vs Nonparametric models, probability distributions, Non-parametric Bayesian Methods, Dirichlet Process, and Python (and possibly R) libraries for Nonparametric Bayesian Models. The teaching method involves a 27-minute talk presented at the EuroPython Conference. The course is intended for individuals interested in expanding their knowledge of Bayesian modeling and machine learning techniques.