Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Covers ANOVA, mediation, and moderation analyses in R for CS-411 Digital Education.
Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
