Bayesian Networks with pgmpy

Abstract: This will be a hands-on workshop on Probabilistic Graphical Models and specifically Bayesian Networks. We shall learn about Bayesian Inference, PGMs, and learn Bayesian Networks with the open source library, pgmpy for which we are contributors. The students shall build various models such as Credit Approval Model, Fraud Models using python and the open source library. The workshop shall teach students basics of bayesian inference, conjugate priors, bayesian networks, features of bayesian networks and then work on a problem.

Bio: Sathwik is a Machine Learning Engineer at Colaberry. He is one of the lead contributors who has actively contributed to shaping and designing the Refactored platform, a proprietary platform built by Colaberry. He is a double master's graduate with academic exposure in various, programming, mathematics, statistic and business disciplines. He has given talks at various Universities including Harvard, UMass Amherst and State University of New York, Buffalo on ML and Data Science concepts. He is also a key contributor and developer who worked on building the workshop on Probabilistic Graphical Models, that was delivered on-site for a Fortune 100 Financial Services client.