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Unsupervised machine learning in engineering and neuroscience

February 9 @ 6:00 pm - 7:30 pm

This  talk with be a set of four short presentations guiding everyone through  three applications of unsupervised machine learning. We begin with the  classic cocktail party problem – how to automatically separate mixed  voices recorded by microphones – presented by Jorge Yanar. This will be  followed by a brief, intuitive explanation of the algorithm used to  perform the task – Independent Components Analysis (ICA) described by  Professor Mark Albert. Dr. Pavan Ramkumar will demonstrate how the same  technique is applied to filter unwanted noise during neural recordings  using EEG, and Anne Zhao will end with a demonstration of how the same  coding strategy has led to insights in how the brain encodes sensory  information in the early auditory and visual systems. Her demo will  allow participants to develop their own simulated neural codes for  processing visual images. 

The  brief talks will consist of a Jupyter notebook for running code and  displaying results. For those who wish to run the examples during the  talk, it will be necessary to install Jupyter running Python version 3  (the Anaconda Python distribution is recommended to set this up). Links  and setup instructions will be given prior to the talks for people to  follow along on their laptops and try the examples on their own if  desired.

Details

Date:
February 9
Time:
6:00 pm - 7:30 pm
Event Category:
Website:
https://www.meetup.com/acm-chicago/

Venue

Wintrust Hall, Room 908
16 East Pearson
Chicago, IL us

Organizer

ACM Chicago