Bayesian Inference: Part 2

Related lectures (31)

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

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

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

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

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

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

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

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

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

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.