Linear Regression: Statistical Inference and Regularization
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Covers regularization in least-squares problems, promoting optimal solutions while addressing challenges like non-uniqueness, ill-conditioning, and over-fitting.
Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.
