Linear Models for Classification: Logistic Regression and SVM

Related lectures (31)

Page 1 of 4

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

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

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

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

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

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.