Statistical Learning Theory Index

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Notes

Formal theory

• Supervised Learning — task setup, loss functions, generalization
• PAC Learning — probably approximately correct framework, sample complexity, agnostic PAC
• VC Dimension — shattering, growth function, fundamental theorem of statistical learning
• Generalization Bounds and Rademacher Complexity — ERM bound, uniform convergence, SRM, PAC-Bayes

Applied learning theory

• Bias-Variance Analysis — decomposition, underfitting vs overfitting
• Data Splits and Distribution — train/dev/test strategy, distribution mismatch
• Error Analysis — ceiling analysis, error categorization
• Evaluation Metrics — precision, recall, F1, AUC-ROC
• Multi-Task Learning — shared representations, hard/soft parameter sharing
• Orthogonalization — separating tuning concerns
• Transfer Learning — pretrain/fine-tune, domain adaptation

Links

• Foundations

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