Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications. Researchers at WID are developing new methodology and theory for extracting useful information from data which may be noisy or high-dimensional, contain missing elements, or come from a variety of different sensors or streaming input.
Machine learning at WID plays a key role in problems as diverse as understanding the immune system, finding the funniest cartoon captions, and developing driverless cars.
Machine learning is an important component of WID’s Data Science Hub.
How can researchers extract useful information from patient data to develop life-saving treatments while making sure records stay private and protected? A WID collaboration looks for an answer.
WID researchers develop algorithms for many applications and industries, but one side project boasts a more playful goal: Mapping brew preferences.
Systems Biology scientist Sushmita Roy finds and predicts trends in biological networks. Her toolkit includes computer science programs, blogs and other bright minds in the Discovery Building.
UW–Madison Mathematics Professor and Discovery Fellow Jordan Ellenberg provides perspective on programming in math, Google Hangout and why our brains are the ultimate tool for discovery.
