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Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Emphasizes the significance of careful cross-validation in deep neural networks, including the split of data and the concept of K-fold cross-validation.
