Kernel Methods in Machine Learning: Kernel Regression and SVM

Related lectures (32)

Page 1 of 4

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

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

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

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

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

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

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

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

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.