Generalization Theory

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Related lectures (31)

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Bias-Variance Tradeoff in Machine Learning

Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.

Model Selection Criteria: AIC, BIC, Cp

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

Complexity: Approximation-Estimation Trade-off

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

Bias-Variance Tradeoff in Machine Learning

Discusses the bias-variance tradeoff in machine learning, emphasizing the balance between model complexity and prediction accuracy.

Bias-Variance Trade-off

Explores the impact of model complexity on prediction quality through the bias-variance trade-off, emphasizing the need to balance bias and variance for optimal performance.

Model Complexity and Overfitting in Machine Learning

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

Model Assessment: Metrics and Selection

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Bias-Variance Trade-Off

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

Overfitting in Supervised Learning: Case Studies and Techniques

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

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.