The focus of this talk is scalable machine learning using the H2O R and Python packages. H2O 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, and 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), and 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 R package.
R and Python Jupyter notebooks with H2O machine learning code examples will be demoed live and made available on GitHub for attendees to follow along on their laptops.
Speaker(s)
Erin LeDell
Erin LeDell is a statistician and machine learning scientist at H2O.ai, the company that produces the open source machine learning platform, H2O. She is the author of a handful of machine learning–related software packages, including the h2oEnsemble R package for ensemble learning with H2O. Erin received her PhD in biostatistics with a designated emphasis in computational science and engineering from UC Berkeley. Her dissertation focused on scalable ensemble learning and was awarded the 2015 Erich L Lehmann Citation by the UC Berkeley Department of Statistics. 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.