Linear Regression and Least Squares Method

Related lectures (31)

Page 2 of 4

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

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

Covers linear regression, regularization, and probabilistic models in generating labels.

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Explores linear regression analysis of ozone data using statistical models.

Covers the basics of linear regression, focusing on estimation and prediction.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Covers linear regression for estimating train speed using least squares and regularization.