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March 12, 2014

Extracting useful information from very large data sets is challenging. In this workshop, we will focus on the challenges of applying machine learning, data mining, and statistics to massive-scale data sets. 


Examples of topics for discussion include:

• Developing approximations when algorithms don’t scale using sampling, sketching, and other techniques;
• Developing summarization and explanation techniques that show patterns in data in an intuitive and useful way; 
• Evaluating the quality of these methods on new data sets; and
• Integrating massive amounts of unstructured text from social media sites, scientific documents, industrial reports, websites, and other sources. 

Given the current speed at which these large data sets are generated, there is a pressing need for solutions to these challenges; we will discuss applications and related challenges in data mining and machine learning on Big Data and explore potential solutions.

This workshop is part of a series focusing on major challenges when it comes to Big Data as part of the MIT Big Data Initiative at CSAIL. These workshops bring together a select group of thought leaders, from industry, academia and government, to focus on the future of Big Data.

MEMBER WORKSHOP Big Data Analytics: Challenges in Big Data for Data Mining, Machine Learning and Statistics
Date:  Wednesday March 26, 2014
Location:  Massachusetts Institute of Technology, Cambridge, MA, Stata Center, Bldg. 32, 1st floor, room 32-155
Host:  MIT Big Data Initiative at CSAIL
Workshop Organizers:  Prof. Cynthia Rudin MIT Sloan, Prof. Sam Madden MIT CSAIL, Elizabeth Bruce, MIT CSAIL
REGISTRATION:  [members please contact Susana Kevorkova,, for registration details]