Lectures related to Multicollinearity

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Penalization in Ridge Regression

Covers penalization in ridge regression, emphasizing the trade-off between bias and variance in regression models.

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Multilinear Regression: Least Square Fit

Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.

Spiked Matrix Estimation

Covers the AMP algorithm for spiked matrix estimation and its application to low-rank matrix factorization and GLM models.

Linear Algebra: Linear Dependence and Independence

Explores linear dependence and independence of vectors in geometric spaces.