Regularization (mathematics)

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Cross-validation

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Related lectures (30)

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Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

Linear Regression: Regularization

Covers linear regression, regularization, and probabilistic models in generating labels.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Complexity: Approximation-Estimation Trade-off

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

Regularization Methods: Training and Validation Base

Explores regularization methods in neural networks, emphasizing the importance of training and validation bases to prevent overfitting.

Regularization Techniques

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.

Data Representation: BoW and Imbalanced Data

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Linear Classification: Logistic Regression

Covers linear classification using logistic regression, regularization, and multiclass classification.