Model Selection: ROC Curves, Regression Evaluation, Conclusion

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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 Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.