Mathematics of Data: Models and Estimators

Related lectures (30)

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Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores models, learning paradigms, and applications in Mathematics of Data.

Introduces regularization techniques to prevent overfitting in machine learning models.

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

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

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

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores statistical estimation, regression models, and model selection in parametric models.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers the probabilistic model for linear regression and the importance of regularization techniques.