Mathematics for Machine Learning

Catalog description

This course focuses on key areas of mathematics at the foundation of machine learning algorithms. After surveying matrix algebra, vector calculus, numerical optimization, and probability, the course culminates with random walks on graphs and vector space embeddings, tasks vital to the preparation of data for neural networks. Throughout the course, learners will approach topics theoretically and experimentally, augmenting traditional paper-and-pencil analysis by writing code.