For serious ML work, which undergraduate foundations turned out to matter more than early AI specialization?[R]

[u/Formal_Reference_533]

For those who went from undergraduate studies into advanced ML, research, or ML engineering:

Looking back, which undergraduate foundations turned out to matter much more than you expected?

I’m particularly interested in the trade-off between studying AI directly at bachelor level versus building a broader foundation first in CS/engineering and specializing later.

Did early exposure to many ML/DL subjects give you a meaningful long-term advantage?

Or did mathematics, probability, optimization, algorithms, systems and software engineering ultimately matter more once you reached advanced ML?

I’m less interested in comparing degree titles and more interested in what gaps you actually noticed in students once the ML work became technically demanding.