Learning with Limited Labeled Data

Learning with Limited Labeled Data

Thursday, May 23
2:50 PM - 3:30 PM
Dogwood

Being able to teach machines with examples is a powerful capability, but it hinges on the availability of vast amounts of data. The data not only needs to exist, but has to be in a form that allows relationships between input features and output to be uncovered. Creating labels for each input feature fulfills this requirement, but most supervised machine learning opportunities do not come nicely packaged with labeled data.

In classical approaches to this problem, engineered heuristics are used to select “best” instances of data to label in order to reduce cost; the model then learns from this smaller labeled dataset. Recent advancements have extended these approaches to deep learning, enabling models to be built with limited labeled data.

In this talk, we explore algorithmic approaches that drive this capability, and provide practical guidance for translating this capability into production.

Presentation Video

SPEAKERS

Nisha Muktewar
Research Engineer
Cloudera Fast Forward Labs
Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial, Advanced Analytics & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses. She holds a Bachelor of Engineering degree in computer science from University of Pune, India.