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 ...

Even in this day and age, computer learning is far behind the learning capability of humans. A team of researchers seek to shrink the gap, however, developing a technique called “Bayesian Program ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

This is a preview. Log in through your library . Abstract This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends ...

This illustration gives a sense of how characters from alphabets around the world were replicated through human vs. machine learning. (Credit: Danqing Wang) Researchers say they’ve developed an ...

The entire tech industry has fallen hard for a branch of artificial intelligence called deep learning. Also known as deep neural networks, the AI involves throwing massive amounts of data at a neural ...

The old adage that practice makes perfect applies to machines as well, as many of today’s artificially intelligent devices rely on repetition to learn. Deep-learning algorithms are designed to allow ...

Evaluating the impact of program activities on outcomes occasionally involves complex data structures – such as units nested within clusters, and observed longitudinally – and the corresponding ...