The Information Criteria of Takeuchi and Akaike

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

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

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

Explores linear regression analysis of ozone data using statistical models.

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

Covers supervised learning through likelihood maximization to find optimal parameters.