Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Complete descriptions appear in the course catalog.
View the semester course schedule.
- EN.553.111 Statistical Analysis I
- EN.553.112 Statistical Analysis II
- EN.553.171 Discrete Mathematics
- EN.553.211 Probability and Statistics for the Life Sciences
- EN.553.310 Prob & Stats for the Physical and Information Sciences & Engineering
- EN.553.310 Probability & Statistics for the Physical Sciences & Engineering
- EN.553.311 Probability and Statistics for the Biological Sciences and Engineering
- EN.553.4/600 Mathematical Modeling and Consulting
- EN.553.4/617 Mathematical Modeling: Statistical Learning
- EN.553.4/629 Introduction to Research in Discrete Probability
- EN.553.4/630 Introduction to Statistics
- EN.553.4/636 Introduction to Data Science
- EN.553.4/650 Computational Molecular Medicine
- EN.553.629 Introduction to Research in Discrete Probability
- EN.553.6/732 Bayesian Statistics
- EN.553.6/733 Advanced Topics in Bayesian Statistics
- EN.553.720 Probability Theory I
- EN.553.721 Probability Theory II
- EN.553.730 Statistical Theory
- EN.553.731 Statistical Theory II
- EN.553.761 Nonlinear Optimization I
- EN.553.762 Nonlinear Optimization II
- EN.553.764 Modeling, Simulation, and Monte Carlo
- EN.553.765 Convex Optimization
- EN.553.734 Introduction to Nonparametric Estimation
- EN.552.735 Topics in Statistical Pattern Recognition
- EN.552.782 Statistical Uncertainty Quantification