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Event space and refreshments sponsored by: Viacom

Event agenda:

6:30 - 7:00 PM: Networking & Food

7:00 - 7:05 PM: Lighting talk by Alejandro Rojas

7:00 - 8:30 PM: H2O Presentation & Hands-on Workshop

8:30 - 9:00 PM: Socializing

Workshop/talk abstract:

The focus of this talk is scalable machine learning using the H2O R (http://www.h2o.ai/download/h2o/r) and Python (http://www.h2o.ai/download/h2o/python) packages. H2O (https://github.com/h2oai/h2o-3) is an open source, distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface.

Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of Generalized Linear Models, Gradient Boosting Machines, Random Forest, Deep Neural Nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), anomaly detection methods, among others. The ability to create stacked ensembles, or "Super Learners", from a collection of supervised base learners is provided via the h2oEnsemble (https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble) R package.

R and Python scripts/notebooks with H2O machine learning code examples will be demoed live and made available on GitHub for attendees to follow along on their laptops.

Preparation or Prerequisites

Attendees can choose to install the H2O R (http://www.h2o.ai/download/h2o/r) or Python (http://www.h2o.ai/download/h2o/python) package on their computer and follow along with the R and Python scripts. Since H2O is also accessible through a web GUI called H2O Flow (http://www.h2o.ai/product/flow/), R or Python experience is not required.

About the Speaker

Erin LeDell (http://www.stat.berkeley.edu/%7Eledell/) is a Statistician and Machine Learning Scientist at H2O.ai (http://www.h2o.ai/), the company behind the open source machine learning platform, H2O (https://github.com/h2oai/h2o-3). She is the author of a handful of machine learning related software packages (http://www.stat.berkeley.edu/%7Eledell/software.html), including the h2oEnsemble (https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble) R package for ensemble learning with H2O. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc.

Erin is also the founder of the Bay Area Women in Machine Learning & Data Science (http://www.meetup.com/Bay-Area-Women-in-Machine-Learning-and-Data-Science/) meetup group, the first chapter of the WiMLDS (http://wimlds.org/) organization.

Erin LeDell on Twitter: @ledell (https://twitter.com/ledell)