BIDS Data Science Lecture Series | March 11, 2016 | 1:10-2:30 p.m. | 190 Doe Library, UC Berkeley
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.