Bayesian ML and Probabilistic Programming: Introduction

In this seminar, we go back to the very basics with Surya, who will (re)visit the fundamentals of the Bayesian approach to ML, including Maximum Likelihood and Maximum A Posteriori Estimation. He will introduce approximate Bayesian inference using PyMC3 (a Probabilistic Programming Language), which will serve as a springboard for understanding future material in this stream, such as Variational Methods, Graphical models, etc.

After a follow-up talk with some applications and other methods (and after the summer pause) we will start a new reading group and a series of seminar talks on PPLs and more advanced Bayesian methods. If you think you might be interested but Bayes' theorem is a mystery to you, don’t miss this one! (and the following ones)

See: CS4780, Intro to ML class at Cornell

In this series