Regression and Classification

Related lectures (32)

Page 3 of 4

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Covers the analysis of various data attributes and linear regression models.

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

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

Explores Generalized Linear Models theory, including logistic and Poisson regression, model evaluation, and coefficient tests.

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

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.