SVMs and Feature Maps

Related lectures (30)

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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

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

Introduces regularization techniques to prevent overfitting in machine learning models.

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

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

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

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

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

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.