Lecture Notes on High Dimensional Linear Regression
Methodology
2025-12-05 v2 Computation
Machine Learning
Abstract
These lecture notes cover advanced topics in linear regression, with an in-depth exploration of the existence, uniqueness, relations, computation, and non-asymptotic properties of the most prominent estimators in this setting. The covered estimators include least squares, ridgeless, ridge, and lasso. The content follows a proposition-proof structure, making it suitable for students seeking a formal and rigorous understanding of the statistical theory underlying machine learning methods.
Cite
@article{arxiv.2412.15633,
title = {Lecture Notes on High Dimensional Linear Regression},
author = {Alberto Quaini},
journal= {arXiv preprint arXiv:2412.15633},
year = {2025}
}