Linear Models for Classification: Recap and Binary Classification

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

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Explores linear models, least squares, Gaussian vectors, and model selection methods.