The Frozen North


Course information:


Professor: Julius Kuti
Office hours: M/W 3:20-4:00 p
(in the lab, or by appointment)

Office: 5410 Mayer Hall
Phone: 534-6096

Teaching Assistant:
David Moore

Administrative Assistant: Bernie Camberos
Office: 5581 Mayer Hall
Phone: 534-7142

ACMS Instructional User Services:


Links to ACS:


Course Overview


This course will address basic methods and modern developments in computational statistics including the frequentist and Bayesian perspective, linear and nonlinear regression, maximum likelihood method, jackknife and bootstrap, principal component analysis, Markov chain Monte Carlo methods, and a brief introduction to information theory, pattern recognition, and machine learning. It is aimed at upper division undergraduates and first year graduate students. Basic familiarity with calculus and linear algebra are the only prerequisites. Some familiarity with probability concepts is helpful though not needed as the lectures include a self-contained introduction to basic probability theory. Matlab tools will provide the computational framework.

Plan for the course material