3.0

credits

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This course explores modern perspectives on generalization in machine learning, connecting classical statistical learning theory with surprising behaviors of deep and overparameterized models. We study both the theoretical foundations and empirical phenomena, including double descent, benign overfitting, and sequential analysis. Students will engage with recent research papers and carry out projects analyzing generalization in real or simulated settings.