I've taken reasonably advanced math courses (I was, for a time, a math major), and I have a great deal of difficulty with machine learning papers. They often reference domain-specific formulas that are prerequisites, sometimes without naming them, and rarely does a paper actually define every symbol used in a formula (in fact, I don't think I've ever seen a paper do this - it's usually none, and they only define a few elements of the notation). The worst part is that they'll use a known formula, but change the notation slightly without explaining why.
These are bad academic habits. If you're using a formula with a dozen variables - at least state what they are, as well as any more unusual or overloaded notation, subscripts, etc. If you're using a formula from elsewhere, it's better to restate what these symbols mean in your paper, rather than send the reader off on a hunt. Be clear, be explicit, leave no doubt. You don't have to explain all of probability theory, but it takes only a line or two to vastly improve the readability of your paper.
Machine learning papers are a particularly toxic brew of people in a rush, who like to show off, and who spend all day fiddling with code & little time reading or writing mathematics. Lots of formulas for quite simple things are at best ambiguous and at worst simply incorrect.
These are bad academic habits. If you're using a formula with a dozen variables - at least state what they are, as well as any more unusual or overloaded notation, subscripts, etc. If you're using a formula from elsewhere, it's better to restate what these symbols mean in your paper, rather than send the reader off on a hunt. Be clear, be explicit, leave no doubt. You don't have to explain all of probability theory, but it takes only a line or two to vastly improve the readability of your paper.