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Wednesday, November 11 • 10:30am - 11:00am
Generalized low rank models

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Across business and research, analysts seek to understand large collections of data organized as a table with numeric, Boolean, and categorical values. Many entries in the table may be noisy or even missing altogether. Low rank models facilitate understanding of tabular data by producing a condensed vector representation for every row and column in the data set. These representations can then be compared, clustered, plotted, and used in subsequent analysis. In this presentation, we will describe what a low rank model is and demonstrate how to build them in H2O. Through examples, we'll see how to fit low rank models to numeric and categorical data sets with missing values, and how to use these models to identify important features and make better predictions.

Speakers
avatar for Anqi Fu

Anqi Fu

Math Hacker, H2O.ai
| Anqi is a math hacker at H2O, where she implements and tests distributed machine learning algorithms.  Anqi worked on network security algorithms with the founder of RioRey, and spent summers conducting physics research at the Naval Research Laboratory and NIST. Anqi holds Master's degrees in Statistics and Economics from Stanford University, and a Bachelor's in Electrical Engineering from the University of Maryland, College Park.
avatar for Madeleine Udell

Madeleine Udell

Postdoctoral Fellow, Caltech Center for the Mathematics of Information
Madeleine Udell is a postdoctoral fellow at Caltech's Center for the Mathematics of Information, hosted by Joel Tropp. She will be joining Cornell as an Assistant Professor in the School of Operations Research and Information Engineering in July 2016. Her research focus is on modeling and solving large-scale optimization problems and on finding and exploiting structure in high dimensional data, with applications in marketing, demographic... Read More →


Wednesday November 11, 2015 10:30am - 11:00am
Ramanujan Stage