Kernel Regression: Weighted Average and Feature Maps

Related lectures (32)

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

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

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Explores Kernel Ridge Regression, the Representer Theorem, and the Kernel Trick in machine learning.

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.

Summarizes the result for linear regression and introduces Vapnik-Chervonenkis dimension and growth function.

Covers advanced topics in Generalized Linear Models, focusing on link functions, error distributions, and model interpretation.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Page 2 of 2