A theory about how models learn from samples and still work on new cases.
SLT is the coach who ignores your lucky half-court shot. It asks, can you score again on Saturday?
It helps explain generalization, overfitting, and sample size. You meet it in model training and testing.
Bias-Variance Tradeoff
SLT explains the cost when a model is too simple or too complex.
ERM
ERM is a key base for Statistical Learning Theory.
Regularization
Regularization can reduce overfitting and help models work on new data.
Scaling-law
Both study how data size links to model performance.