Real World Machine Learning Uses in Our Community

Next Event for Technology Association of Louisville Kentucky : April 14th

Real World Machine Learning Uses in Our Community, Presented By Behnaz Abdollahi

***$12 at door, cash or credit card for your all-you-can-eat pizza and drink; Cash Bar for Beer/Wine/Cocktails***

Tuesday, April 14, 2015 from 6:30 PM to 8:30 PM (EDT)

At Loui Loui’s in Jeffersontown, KY

  10212 Taylorsville Rd, Jeffersontown, KY 40299
(502) 266-7599

Host:  Owner of Loui Loui’s>Michael Spurlock, A Prominent Louisville Techie

To RSVP, click now: An Eventbrite event

Behnaz Abdollahi is a U of L PhD candidate with practical, real world experience in our city’s healthcare circles regarding Date Mining and Machine Learning.  She has studied and developed several practical clinical applications: automatically detecting vessel ranking of tumor, analyzing survival rate of lung cancer patients, and analyzing lung transplant acceptance patients. She has been published in scientific journals, and given presentations at workshops and conferences across the country.  In addition to her research, she is a software engineer and was working in industry before starting her PhD. She is expert in many programming languages such as: R, Python, Octave, Java and Matlab. Behnaz is a dedicated and motivated technical woman, and this session is not to be missed. She is a PhD candidate at University of Louisville graduating Spring 2015, beginning this phase of education with a fellowship award to fully pay her tuition and stipend.

Machine learning techniques are widely used to model complicated datasets that statistical methods  cannot analyze them. These techniques learn a mathematical model from previous instances of data and predict the behavior of future and unknown inputs.  Healthcare datasets are one of the most complicated datasets.  Several factors need to be considered  for every patient. Conventional statistical techniques are not good enough to convert the raw data into reusable knowledge.  The challenges also include the fact that measuring some of the disease factors are costly, and the volume of data is massive. Come learn what has been done in recent times in current projects in our community.

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