Kernel Methods: SVM and Regression

Related lectures (31)

Page 3 of 4

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

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

Covers linear models, multi-class classification, k-Nearest Neighbors, and feature expansion techniques.

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Covers random features in neural networks and kernel regression equivalence.

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.