Covers the role of models and data in statistical learning and optimization formulations, with examples of classification, regression, and density estimation problems.
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.
