Scientific Machine Learning — From a Trained Model to a Defensible Claim
Aman Bhargava
For engineers who can already build systems and now need to make empirical claims that survive contact with statistics, messy data, and production.
First working edition, published as a standalone PDF on Aman's authorization. Strongest known limitation: the public evidence and reproduction routes and an accessible reviewed equivalent are still in preparation; the executable companion, evidence dossiers, and accessible edition follow separately once their release gates pass.
Integrity
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