Parametric Models

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Probabilistic Models for Linear Regression

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

Parametric Models: Regression Estimators and Optimization

Covers parametric models, regression estimators, and optimization in statistical modeling.

Linear Regression: Statistical Inference and Regularization

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

Parametric Models: Mathematics of Data

Explores parametric models in data analysis, covering regression estimators, optimization problems, and statistical models.

Generalized Linear Regression: Classification

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

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Model Selection Criteria: AIC, BIC, Cp

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

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.