Cold Start Problem - The Biggest Data Scientist's Dilemma

Solving the Data Scientist's Dilemma - The Cold Start Problem

Presented By Kirk Borne
Kirk Borne
Kirk Borne
Principal Data Scientist And Executive Advisor, Booz Allen Hamilton

Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. Kirk spent nearly 20 years supporting NASA projects.

Presentation Description

Supervised machine learning is a great tool when you have labeled training data and known classes that you are trying to predict for new previously unseen data. But, the assumptions of labeled data and known classes are generally not true in unsupervised machine learning. So, how can you maximize the data science outcomes, benefits, and applications when faced with the cold start problem? We will discuss this challenge and some solutions with several illustrative examples.

Presentation Curriculum

Solving the Data Scientist's Dilemma - The Cold Start Problem
36:59
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