Linear Regression: Basics

Related lectures (48)

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

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

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

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

Explores nonlinear regression using Gaussian components for density modeling.

Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Page 3 of 3