Lectures related to Loss Functions and Optimization

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

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

Explores linear regression in high dimensions and practical house price prediction from a dataset.