Application of stochastic process theory to design and analyze algorithms used in statistics and machine learning, especially Markov chain Monte Carlo and stochastic optimization methods; connects theoretical results to practice through proofs, numerical experiments, and expository writing.

Level

grad

Department

CDS

Credits

4

Prerequisites

DS 122; MA 581/CS 237/EK 381