Mathematical Methods in Data Science II

4 credits

This is the second course in a three-course sequence in mathematical methods in data science. Topics include analysis on graphs, random walks and diffusion geometry for uniform and non-uniform sampling, eigenvector perturbation, multi-scale analysis of data, concentration of measure phenomenon, binary embeddings, quantization, topic modeling, and geometric machine learning, as well as scientific applications. Some scientific programming experience is recommended. Prerequisites: MATH 216A. Students who have not completed MATH 216A may enroll with consent of instructor.