MIT duo uses music, videos, and real-world examples to teach students the foundations of .
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Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats.
“It was cool,” she says. “It didn’t sound at all like a machine had made it.”
This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center’s Kirsch Auditorium during Independent Activities Period (IAP). But the opening lecture featured a twist: a recorded welcome from former President Barack Obama. The video was quickly revealed to be an -generated fabrication, one of many twists that Alexander Amini ’17 and Ava Soleimany ’16 introduce throughout their for-credit course to make the equations and code come alive. As hundreds of their peers look on, Amini and Soleimany take turns at the podium. If they appear at ease, it’s because they know the material cold; they designed the curriculum themselves, and have taught it for the past three years. The course covers the technical foundations of and its societal implications through lectures and software labs focused on real-world applications. On the final day, students compete for prizes by pitching their own ideas for research projects. In the weeks leading up to class, Amini and Soleimany spend hours updating the labs, refreshing their lectures, and honing their presentations.
A branch of , harnesses massive data and algorithms modeled loosely on how the brain processes information to make predictions. The class has been credited with helping to spread machine-learning tools into research labs across MIT. That’s by design, says Amini, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS), and Soleimany, a graduate student at MIT and Harvard University.
Both are using in their own research — Amini in engineering robots, and Soleimany in developing diagnostic tools for cancer — and they wanted to make sure the curriculum would prepare students to do the same. In addition to the lab on developing a music-generating , they offer labs on building a face-recognition model with convolutional neural networks and a bot that uses reinforcement learning to play the vintage Atari video game, Pong. After students master the basics, those taking the class for credit go on to create applications of their own.
This year, 23 teams presented projects. Among the prize winners was Carmen Martin, a graduate student in the Harvard-MIT Program in Health Sciences and Technology (HST), who proposed using a type of neural net called a graph convolutional network to predict the spread of coronavirus. She combined several data streams: airline ticketing data to measure population fluxes, real-time confirmation of new infections, and a ranking of how well countries are equipped to prevent and respond to a pandemic.
“The goal is to train the model to predict cases to guide national governments and the World Health Organization in their recommendations to limit new cases and save lives,” she says. […]
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