Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

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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.

Parametric Models

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

Probability Models: Fundamentals

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Generalized Linear Regression: Classification

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

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Weighted Least Squares Estimation: IRLS Algorithm

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

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.