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Notice of Machine Learning Contest Changes

Dear Friends and Colleagues:

We are making changes to the Machine Learning Contest based on feedback from the community. Simply put, contestants have stated that significantly more time is needed to deliver a novel, valuable and robust solution. Developing and delivering a valuable mobile app using machine learning techniques is more complex than the current time-line was going to allow and we desire the best possible mobile app with information and features the public does not currently have available.

As a result, we are re-scheduling the awards ceremony to be held on December 5, 2015 at Level 3 Communications. The awards ceremony will be held live in a 500 person large auditorium and live-streamed to a global audience. Machine learning techniques shall be published. Contestants register here. Sponsors may register here or seek more information about sponsorship levels by emailing

We will kick-off the contest on our original date of Saturday February 28, 2015 at Level 3 Communications with data science and engineering presentations and provide detailed contest rules and information on that date both live and published on the web-site. The event will be live-streamed globally and you are welcome to register here.

Data science & engineering presentations include:

  • Paul Balas: Big Data and Classification
  • Michael Malak: Spark New Release
  • Charles Clifford: Resource-Oriented Clinical Information Repository
  • Lee Cole: Real-time Analysis for High-Frequency Trading
  • Michael Walker: Data Science Laws
  • Chris Howard: Making Sense of Sensor Data
  • William Stanton: Deep Learning
  • Ken Farmer: Data Quality Solutions
  • Florian Sobieczky: Scale Space Theory
  • Andrew Weekley: Anomaly Detection Algorithms
  • Tom Flaherty: Reactive Streams with RxJS
  • John Dougherty: Hadoop Ecosystem
  • Jennifer Evans: Using Event Data to Enhance Analytic Models

We apologize for any inconvenience and please remember that machine learning is all about experimentation, finding what doesn't work, and refining the model to get to a solution.


Machine Learning Contest Team