Model Selection Criteria: AIC, BIC, Cp

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Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores linear regression analysis of ozone data using statistical models.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.