A good physics or math grad who has done bayesian stats is at no disadvantage on the machine learning stuff, but what do you mean by “belief networks background”?
There is ton of knowledge about probabilistic processes defined by networks in various ways, numerical methods for inference in them, clustering, etc. All the fundamental stuff in this range has applications to physics, and some of it was known in physics before getting reinvented in machine learning, so in principle a really good physics grad could know that stuff, but it’s more than standard curriculum requires. On the other hand, it’s much more directly relevant to probabilistic methods in machine learning. Of course both should have good background in statistics and bayesian probability theory, but probabilistic analysis of nontrivial processes in particular adds unique intuitions that a physics grad won’t necessarily possess.
Machine learning, more math/probability theory/belief networks background?
A good physics or math grad who has done bayesian stats is at no disadvantage on the machine learning stuff, but what do you mean by “belief networks background”?
Do you mean “deep belief networks”?
There is ton of knowledge about probabilistic processes defined by networks in various ways, numerical methods for inference in them, clustering, etc. All the fundamental stuff in this range has applications to physics, and some of it was known in physics before getting reinvented in machine learning, so in principle a really good physics grad could know that stuff, but it’s more than standard curriculum requires. On the other hand, it’s much more directly relevant to probabilistic methods in machine learning. Of course both should have good background in statistics and bayesian probability theory, but probabilistic analysis of nontrivial processes in particular adds unique intuitions that a physics grad won’t necessarily possess.