Best Universities offering graduate programs in Statistics and Machine Learning

Top Ranked Graduate Programs in Statistics and Machine Learning

Ranked as

1 in Best National University

Tuition

$47,140

State

NJ

Acceptance

6.41%

Research across the disciplines increasingly requires the integration of data science, statistics and machine learning to make cutting-edge advancements. Princeton University is dedicated to playing a vital role in preparing students to lead in these areas, and the Center for Statistics and Machine Learning (CSML) is a campus focal point for fulfilling this commitment. The center’s mission is threefold: to foster and support a community of scholars addressing the challenges of data-driven research; to educate students in the foundations of modern data science including computation, machine learning, and statistics, along with specific application domains; and to develop innovative methodologies for extracting information from data.
The Center for Statistics and Machine Learning is a focal point for education and research in data science at Princeton University. By its nature, CSML is an interdisciplinary enterprise. The center supports and collaborates on research and teaching that combines insights from computation, machine learning, and statistics with specific application domains.

Ranked as

12 in Best National University

Tuition

$52,170

State

MD

Acceptance

12.54%

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.
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.553.400 Mathematical Modeling and Consulting EN.553.417 Mathematical Modeling: Statistical Learning EN.553.429 Introduction to Research in Discrete Probability EN.553.430 Introduction to Statistics EN.553.436 Data Mining EN.553.450 Computational Molecular Medicine EN.553.620 Probability Theory I EN.553.621 Probability Theory II EN.553.629 Introduction to Research in Discrete Probability EN.553.630 Statistical Theory EN.553.631 Statistical Theory II EN.553.632 Bayesian Statistics EN.553.6 Advanced Topics in Bayesian Statistics EN.553.661 Foundations of Optimization EN.553.662 Optimization Algorithms EN.553.664 Modeling, Simulation, and Monte Carlo EN.553.665 Convex Optimization EN.553.734 Introduction to Nonparametric Estimation EN.552.735 Topics in Statistical Pattern Recognition EN.552.782 Statistical Uncertainty Quantification.