Lectures related to Estimation, Shrinkage and Penalization

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.