SVMs and Feature Maps

Related lectures (30)

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Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

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

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

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

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