Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
