#learning-theory

2 posts

ai deep-dive Reading Stanford CS229 Aug 22, 2026

Generalization: Bias–Variance, Double Descent, and Sample Complexity

Chapter 8 decomposes test MSE into irreducible noise, squared bias, and variance, then uses uniform convergence and VC dimension to explain when training performance transfers to new data. Double descent shows why parameter count is not a universal measure of complexity.

#cs229 #machine-learning #generalization #bias-variance #learning-theory

learning guide Reading CMU 10-301 Machine Learning Aug 22, 2026

CMU 10-301 HW6: Learning Theory, MLE/MAP, and Fairness Metrics

HW6 combines generalization, MLE/MAP, probabilistic learning, fairness metrics, and social impact in one written assignment about assumptions and tradeoffs.

#cmu #machine-learning #learning-theory #ai-ethics