Linear Regression: Statistical Inference Perspective

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Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Statistical Theory: Maximum Likelihood Estimation

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Parametric Models: Regression Estimators and Optimization

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

Parametric Models

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