Generalized Linear Models: Examples and Applications

Related lectures (31)

Page 3 of 4

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores overdispersion, model assessment, and regression techniques for count data.

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

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

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

Explores statistical estimation, regression models, and model selection in parametric models.

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

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

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