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Related lectures (31)

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

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

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.

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

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