Polynomial Regression: Basics and Regularization

Related lectures (30)

Page 2 of 3

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

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

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

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

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

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

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.