Hortonworks Chief Data Scientist: Bayesian networks with R and Hadoop

This meetup is co-hosted with Palo Alto Data Science Association.

If you are remote you can watch live at https://new.livestream.com/pemo/bayesiannetworks

In modeling intelligent systems for real world applications, one inevitably has to deal with uncertainty. Bayesian networks are well established as a modeling tool for expert systems in domains with uncertainty, mainly because of their powerful yet simple representation of probabilistic models as a network or graph. They are widely used in fields such as genetic research, healthcare, robotics, document classification, image processing and gaming. Working with large-scale bayesian networks is a computationally-intensive endeavor. In this talk I will describe my experience working with R and Hadoop to implement large scale bayesian networks: 1. Quick overview of bayesian networks and example applications 2. Building a bayesian network: by hand and with automated learning algorithms 3. Inferring data from a bayesian network 4. Dealing with large-scale networks with R and Hadoop You will learn about some of the advantages of bayesian networks for real-world datasets, as well as about the challenges of building and using large-scale bayesian networks using R and Hadoop.

Ofer Mendelevitch is Director of data science at Hortonworks, where he is responsible for professional services involving data science with Hadoop. Prior to joining Hortonworks, Ofer served as Entrepreneur in Residence at XSeed Capital where he developed an investment strategy around big data. Before XSeed, Ofer served as VP of Engineering at Nor1, and before that he was Director of engineering at Yahoo! where he led multiple engineering and data science teams responsible for R&D of large scale computational advertising projects including CTR prediction (with Hadoop), a new front-end ad-serving system and sales tools.