Concept

Generalization error

Related lectures (31)

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.

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

Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

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

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

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Explores overparameterization, generalization, overfitting, underfitting, and implicit regularization in deep learning models.

Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Explores the control of complexity in hypothesis spaces and the trade-off between approximation and estimation in risk decomposition.

Introduces supervised learning concepts and the k-Nearest Neighbors method for classification and regression tasks.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.