Linear Regression: Maximum Likelihood Estimation

Related lectures (31)

Page 2 of 4

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

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

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

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

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

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

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

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