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Introduction to Probabilistic Machine Learning with PyMC3

Event Information



Date: April 26, 2018

Place: McLean, VA @ Haystax Technology

Speaker: Daniel Emaasit



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. This meetup will introduce probabilistic machine learning and probabilistic programming with PyMC3. We will discuss the basics of machine learning from a probabilistic/Bayesian perspective and contrast it with traditional/algorithmic machine learning.

We will also discuss how to build probabilistic models in computer code using a new exciting programming paradigm called Probabilistic Programming (PP). Particularly we shall use PyMC3, a PP language, to build models ranging from simple generalized linear models to complex hierarchical models and nonparametric models for machine learning.


Only a laptop with a modern web browser like Google chrome or Mozilla Firefox. Click this icon Binder to open the notebooks in a web browser without installing any software.

Speaker Bio:

Daniel Emaasit is a Data Scientist at Haystax Technology. His interests involve developing principled probabilistic models for problems where training data are scarce by leveraging knowledge from subject-matter experts and context information. In particular, he interested in flexible probabilistic machine learning methods, such as Gaussian processes and Dirichlet processes, and data-efficient learning methods such as Bayesian optimization.

Daniel is also a Ph.D. Candidate of Transportation Engineering at UNLV where his research in nonparametric Bayesian methods is focused on developing flexible-statistical models for traveler-behavior analytics.