STAT 251b / STAT 551b/ENAS 496 Stochastic Processes
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.
STAT 262 Computational Tools for Data Science
Introduction to the core ideas and principles of modern data analysis, bridging statistics and computer science, and providing tools for changing methods and techniques. Topics include principle component analysis, independent component analysis, dictionary learning, neural networks, clustering, streaming algorithms (streaming linear algebra techniques), online learning, large scale optimization, simple database manipulation, and implementations of systems on distributed computing infrastructures. Students require background in linear algebra, multivariable calculus, and programming.
STAT 669 Statistical Learning Theory
Introduction to theoretical analysis of machine learning algorithms. Focus on the statistical and computational aspects. Will cover subjects such as decision theory, empirical process theory, and convex optimization. Prerequisites linear algebra, multivariable calculus, stochastic processes, and introduction to machine learning such as Stat 365b or a similar course.