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I did a master's in econometrics and that was my experience. In the last year of my undergrad, I took a course in machine learning that seemed to have way more intellectual rigor than any of the material covered in grad school.

At one point, I mentioned to the professor that I was concerned that the model he had presented was overfitting, and he had no understanding of what the term meant. I think that economics studies fascinating problems (how do people make choices? What are the optimal choices for policy makers to make?) but economists approach the problem completely wrong.



With deep learning overfitting has been thrown out of the window..


Obviously overfitting is always a concern, but deep learning has (when done correctly) found ways to mitigate concerns about overfitting.

Dropout, for instance, or k-fold cross validation.




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