Lectures related to Linear Models and Overfitting

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

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

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.