Generalized Linear Models: Theory and Applications

Related lectures (32)

Page 3 of 4

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

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

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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