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ODSC Boston: Understanding Machine Learning Results to Increase their Value & Avoid Pitfalls

  • CIC Cambridge, Mosaic, 3rd fl 245 Main Street Cambridge, MA, 02142 United States (map)

Speaker: Linda M. Zeger, Ph.D., Founder / Principal Consultant at Auroral LLC

Understanding Machine Learning Results to Increase their Value & Avoid Pitfalls

6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments
6:30pm - 7:20pm - Talk
7:20pm - 7:30pm - Q&A
7:30pm - 8:00pm - Networking

Dr. Linda M. Zeger leads the design and execution of innovative processes and solutions to derive maximum value from data. Through projects she has led in scheduling for data delivery protocols, communication and sensor networks, and healthcare analytics, she has developed techniques to substantially improve network efficiency and reliability, guide system operations, and derive key insights, by employing statistical modeling, machine learning, and data analytics.

Dr. Zeger is the founder and principal consultant of Auroral LLC, and she has also held positions at MIT Lincoln Laboratory, Lucent Technologies, Educational Testing Service, and with universities. Dr. Zeger earned a Ph.D. in physics from Harvard University. She is is the author of numerous published papers, and is an inventor on a number of patents.

With the increasing use of artificial intelligence throughout many industries, much excitement has been generated over the potential benefits that can be obtained. At the same time, questions have arisen regarding potential risks of artificial intelligence and its usage of personal data. This session will describe issues that should be considered before and while employing such a system, in order to better understand, and thereby increase, its utility, as well as to avoid possible pitfalls.

The quantity and quality of data used to train a machine learning system, as well as the quality and relevancy of the data on which the system is employed, has a great effect on the value of a machine learning system. A thorough understanding of the information content, type, and quality of the input data, as well as how it was selected, obtained, and cleansed is also essential to elucidate any potential inaccuracies, biases, or limitations in the results produced by machine learning.

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