Top Ranked Graduate Programs in Statistics and Machine Learning

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 Graduate Certificate in Statistics and Machine Learning is designed to formalize the training of students who both contribute to, or make use of, statistics and machine learning as a significant part of their research.

Artificial intelligence systems and machine-learning algorithms have come under fire recently because they can pick up and reinforce existing biases in our society, depending on what data they are programmed with. Zou co-authored the paper with history Professor Londa Schiebinger, linguistics and computer science Professor Dan Jurafsky and electrical engineering graduate student Nikhil Garg, who was the lead author. Schiebinger said she reached out to Zou, who joined Stanford in 2016, after she read his previous work on de-biasing machine-learning algorithms.

PRiML.upenn is a joint effort of Penn Engineering and Wharton, and brings together the large and diverse machine learning community at Penn. The forum also hosts an annual spotlights session, which features short spotlight talks on work by PRiML.upenn members over the preceding year.

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.

Two computer scientists at the UCLA Samueli School of Engineering have received a four-year, $947,000 research grant from the National Science Foundation to make machine learning a branch of artificial intelligence where computer programs learn and improve on their own widely available and easier to work with. Machine learning has been really successful in the past decade, leading to state-of-the-art techniques for language translation, face recognition and other compelling applications, but these advances have mainly come from experts with specialized knowledge at major technology companies and at universities, said Todd Millstein, professor of computer science and the principal investigator on the research. To change that paradigm, the UCLA computer scientists combine two strengths to help make machine learning accessible Millstein brings expertise in software programming, and co-principal investigator Guy Van den Broeck, an assistant professor of computer science, specializes in artificial intelligence and its applications.

Electrical Engineering: Signal Processing and Machine Learning, M.S. The Signal Processing and Machine Learning (MLSP) program is intended for students looking for a jump-start on a career in data science, with a passion for quantitative thinking, practical problem solving, computer programming, and applications to a variety of domains. The combined focus on the mathematical foundations of data science and their practical application to real-world problems will prepare graduates to be ready to immediately contribute in a variety of different MLSP jobs.

Machine learning for the design of elastic metamaterials. The design space offered by these different types of engineered materials is virtually unbounded and, for the most part, unexplored. PhD in Mechanical Engineering, Civil Engineering, Aerospace Engineering, or closely related fields.

Using Machine Learning to Predict Damage Tolerance in Unstable Systems: a Case of Impacted Batteries and Exploding Materials. Significant advances have been made in designing such systems in a way that initial design is safe and performance is guaranteed. Candidates should have earned a doctorate in Physics, Chemical Engineering, Chemistry, Aerospace Engineering, Materials or Mechanical engineering or a related field.

Get on the fast track to make an impact in one of today’s fastest growing fields with a master’s degree in machine learning. The machine learning master’s program establishes the theoretical and practical foundations necessary to be at the forefront of progress in the next technological revolution. Advancements made in machine learning and related disciplines will soon touch every piece of technology, making an advanced degree an essential asset for a successful career.