Description
In this tutorial, we review mathematical principles from statistics, optimization, and linear algebra that form the basis of modern machine learning tools. Specifically, we discuss concepts from estimation, numerical optimization, matrix analysis, kernel methods, and convolution operations and highlight how the combinations of these are used to conduct machine learning tasks (e.g., computer vision, classification, regression, data reduction, reinforcement learning).