Lectures related to Variable Selection Methods

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

The Information Criteria of Takeuchi and Akaike

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Probabilistic Models for Linear Regression

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

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Regression Methods: Model Building and Diagnostics

Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.

Linear Regression: Basics and Applications

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

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.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.