For humans and machines, intelligence requires making sense of the world — inferring simple explanations for the mishmosh of information coming in through our senses, discovering regularities and ...

Scientific machine learning combines the areas of artificial intelligence and scientific computation. Because scientific machine learning algorithms are informed and constrained by physics laws, they ...

Professor Daniel Acuna is an Associate Professor in the Department of Computer Science at the University of Colorado at Boulder. He leads the Science of Science and Computational Discovery Lab. He ...

The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...