Lecture
Regularization: Promoting Optimal Solutions
Related lectures (30)
Regression: Simple and Multiple Linear
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Supervised Learning in Financial Econometrics
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Error Decomposition and Regression Methods
Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.
Linear Regression: Basics and Estimation
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Sparse Regression
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Regularization Techniques
Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.
Feature Engineering: Polynomial Regression
Covers fitting linear regression on features of the original predictors for flexible feature representation.
L1 Regularization: Sparse Solutions and Dimensionality Reduction
Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.
Polynomial Regression: Overview
Covers polynomial regression, flexibility impact, and underfitting vs overfitting.