Gaussian Mixture Regression: Modeling and Prediction

Related lectures (31)

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Covers the multivariate normal distribution, properties, and sampling methods.

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

Covers supervised learning through likelihood maximization to find optimal parameters.

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

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

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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