Linear Models & k-NN

Related lectures (31)

Page 1 of 4

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

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

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

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

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Covers linear models for classification, focusing on logistic regression and support vector machines.

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