Gaussian Process Regression: Probabilistic Linear Regression

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

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

Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Explores statistical estimation, regression models, and model selection in parametric models.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

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

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.