Lectures related to Regularization: Promoting Optimal Solutions

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Comparing L1 and L0 + Greedy algorithms

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Linear Regression

Covers linear regression for estimating train speed using least squares and regularization.

Regularization in Machine Learning

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.