Introduction to Probabilistic Machine Learning with Stan

Abstract

Machine Learning has gone mainstream and now powers several real world applications like autonomous vehicles at Uber & Tesla, recommendation engines on Amazon & Netflix, and much more. In this meetup, I introduced probabilistic machine learning and probabilistic programming with Stan. I discussed the basics of machine learning from a probabilistic/Bayesian perspective and contrasted it with traditional/algorithmic machine learning. I also discussed how to build probabilistic models in computer code using a new exciting programming paradigm called Probabilistic Programming (PP). Particularly I used Stan (within R), a PP language, to build models ranging from simple generalized linear models to complex hierarchical models and nonparametric models for machine learning.

Date
Location
Las Vegas, Nevada

Pre-requisites:

The following software tools are required to run the demo(s):

  1. R + RStudio:- Follow this link to install R. Also install RStudio.
  2. rstan:- Follow this link to install rstan.
  3. bayesplot:- Follow this link to install bayesplot