[aje403]

The average CS student didn't do that great in LA/Calc. Most CS programs don't require vector calc. At an average uni, the vector calc class/linear algebra will be extremely forgiving and you really don't need to understand the material. I don't think any undergrad CS program in the country teaches enough statistics and probability to understand some of the harder latent models or generative modeling. The math can get arbitrarily hard enough where a lot of decently ranked universities don't even teach the classes required to understand some papers, but that's overkill. An Udacity gold star isn't really a replacement for that either.

So basically, people like you are a huge risk for a company. You really can't prove you have anything more than a practical ability to use the algorithms. Most algorithms are hard to implement from scratch (robustly, let alone correctly), and you would be doing so by reading papers you have a scant understanding of..

I'd bet a large sum of money 2/3 of the candidates applying for a machine learning role without a PhD could not even provide any derivation of OLS or something like that if it came up in an interview.

Incoming downvote train, choo choo

[jlelonm]

What're some examples of a "harder latent model"

[aje403]

Here’s a really hyperbolic example: https://arxiv.org/pdf/0908.4425.pdf