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Related lectures (31)

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Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Linear Regression Essentials

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Regression and Classification

Explores regression, classification, linear models, decision trees, and random forests in data analysis.

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

General Linear Model: Model Selection

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.