Lectures related to Polynomial Regression and Gradient Descent

Explores Gaussian mixture regression and overfitting with multiple Gauss functions.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

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

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores binary classification by regression, decision functions, and various cost functions.