Gaussian Mixture Regression: Theory and Applications

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

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

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

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