Typically we post regarding resources for data science researchers, but in case you stumbled upon this blog and are looking for data science programs to get your degree, we recommend exploring this site: Data Science …
Machine Learning
Emotion-Recognizer Demonstration
Interactive Demonstration of the Emotion-Recognizer helps provide an engaged experience and and introduction to applications of machine learning. The Emotion-Recognizer involves using NEXT to map out the space of concepts, simple machine vision tools to …
Machine Learning for Everyone by Rob Nowak
Rob Nowak explains Machine Learning, Neural Networks, Deep Learning, Linear Classifiers, Good Features, Principle Component Analysis, Dictionary Learning, Generalization, Cross Validation in this talk called “Machine Learning for Everyone”
How Can Machine Learning Improve Educational Technologies?
by Blake Mason, John Vito Binzak, and Fangyun (Olivia) Zhao As computer-based technologies continue to establish a significant presence in modern education, it becomes increasingly important to understand how to improve the ways that these …
Interactive Science Communication
by Scott Sievert and Purav “Jay” Patel Scientific research is still communicated with static text and images despite innovations in learning theories and technology. We are envisioning a future in which interactive simulations and visualizations …
Singular Value Decomposition (SVD)
By Lowell Thompson and Ashley Hou This is a collaborative tutorial aimed at simplifying a common machine learning method known as singular value decomposition. Learn how these techniques impact computational neuroscience research as well! Singular …
LUCIDtalks: Machine Learning with Ayon Sen
As part of our LUCID program, the graduate students meet weekly and discuss topics that are interesting to both the computation and behavioral graduate students. In this video Ayon provides an overview of Machine Learning in …
Finding Meaning in Big Data
Discovery Fellows Rebecca Willett and Rob Nowak are creating algorithms to make sense of big data and help machines learn.
How New Yorker Cartoons Could Teach Computers To Be Funny
“The computer models Nowak and his team are developing are called adaptive crowdsourcing algorithms. They attempt to weed out the weakest captions as quickly as possible to get more people to vote on the potential …
The Science of Funny: Active Machine Learning & Cartoons
The New Yorker is using a machine learning system developed by WID Optimization researchers to sort through captions for their weekly cartoon caption contest.