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Related lectures (30)

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

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

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

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

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.