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Related lectures (29)

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Linear Regression: Regularization

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

Back to Linear Regression

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Functional Linear Regression: Sparse Estimation and Adaptive Methods

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

Sparse Regression

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

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Gradient Descent and Linear Regression

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

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