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 ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

The Poisson distribution is widely used in artificial intelligence (AI) and machine learning. In Bayesian inference, probability distributions often help solve problems that would otherwise be ...

Catalog description: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of ...