Linear Regression and Gradient Descent

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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers the basics of linear regression, focusing on estimation and prediction.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

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

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

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

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.