Data-Driven Modeling: Regression

Related lectures (31)

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

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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

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

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

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

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

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