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Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.
Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.
Covers the AMP algorithm for spiked matrix estimation and its application to low-rank matrix factorization and GLM models.
