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 machine learning. It is aimed at upper division undergraduates and graduate students with interest in modern data analysis. 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 some computational examples, but not required for solving the assignments.
Plan for the course material
- Basik probability concepts
- Frequentist and Bayesian views
- Maximum likelihood
- Linear and nonlinear regression
- Jackknife
- Bootstrap
- Markov chain Monte Carlo
- Machine learning