Shrinkage (statistics)

Formal sciences
Statistics
Data analysis
Cross-validation

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Related lectures (24)

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Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Adaptive Gradient Methods: Theory and Applications

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.

SVMs and Feature Maps

Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.

Data Representations & Processing

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Gradient Descent: Optimization and Regularization

Covers the algorithm of gradient descent for optimization, including early stopping and regularization techniques.

Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Data Representations and Processing

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Mathematics of Data: Models and Estimators

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.

Statistical Estimation: Gaussian Linear Model

Delves into statistical estimation, highlighting the Gaussian linear model and the limitations of ML estimators.

Generalized Linear Models: Theory and Applications

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