Title: Scalable Machine Learning for R with MapR
Speaker: Joseph Blue, Director of Global Data Science for MapR
Time and Date: 4:30-6:30pm, Thursday, 15 December 2016
Location: 6C35 (building 6, room C35), University of Canberra
In this discussion, we will review some of the popular
libraries that allow R users to interact with large-scale and
unstructured data sets through the use of a distributed environment.
After a brief intro to deep learning concepts, a demo will be shown that
leverages H2O from R to detect anomalies in recent Australian stock
prices. The RMarkdown notebook and other resources will be provided to
attendees. Additionally, any questions from the audience involving the
use of R in distributed environments can be addressed.
Joe is the Director of Global Data Science for MapR, where
he has focused on collaborating with customers to explore large,
unstructured data sources and derive real business value for the past 3
years. He has deployed solutions in financial, healthcare, advertising,
manufacturing, retail and media markets.
Prior to joining MapR, Joe developed predictive models in health care
for Optum (a division of UnitedHealth) as Chief Scientist. He was the
first Fellow for Optum's start-up, Optum Labs and has several patents
Before his time at Optum, Joe accumulated over 10 years of data
science experience at Fair Isaac, Lexis Nexis, HNC Software and ID
Analytics (now LifeLock) specializing in business problems such as fraud
& anomaly detection, which yielded a patent for identity theft