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Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Explores self-supervised learning, embedding techniques, HSIC, and geometric information in downstream tasks.
Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
