Program in Statistics and Machine Learning
David M. Blei, Computer Science
Jianqing Fan, Operations Research and Financial Engineering
Kosuke Imai, Politics
Robert E. Schapire, Computer Science
John D. Storey, Molecular Biology and Lewis-Sigler Institute for Integrative Genomics
The Program in Statistics and Machine Learning is offered by the Center for Statistics and Machine Learning. The program is designed for students, concentrating in any department, who have a strong interest in data analysis and its application across disciplines. Statistics and machine learning -- the academic disciplines centered around developing and understanding data analysis tools -- play an essential role in various scientific fields including biology, engineering, and the social sciences. This new field of "data science" is interdisciplinary, merging contributions from computer science and statistics, and addressing numerous applied problems. Examples of data analysis problems include analyzing massive quantities of text and images, modeling cell-biological processes, pricing financial assets, evaluating the efficacy of public policy programs, and forecasting election outcomes. In addition to its importance in scientific research and policy making, the study of data analysis comes with its own theoretical challenges, such as the development of methods and algorithms for making reliable inferences from high-dimensional and heterogeneous data. This program provides students with a set of tools required for addressing these emerging challenges. Through the program, students will learn basic theoretical frameworks and apply statistics and machine learning methods to many problems of interest.
Students are admitted to the program after they have chosen a concentration, generally by the beginning of their junior year. At that time, students must have prepared a tentative plan and timeline for completing all of the requirements of the program, including required courses and independent work (as outlined below), as well as any prerequisites for the selected courses. For enrollment or questions contact Tara Zigler, program manager.
Students are required to take a total of five courses and earn at least a B- for each course: one of the "Foundations of Statistics" courses, one of the "Foundations of Machine Learning" courses, and three elective courses. With all necessary permissions, advanced students may also take approved graduate-level courses. Students may count at most two courses from their departmental concentration or another certificate program toward this certificate program.
Students are also required to complete a thesis or at least one term of independent work in their junior or senior year on a topic that makes substantial application or study of machine learning or statistics. This work may be used to satisfy the requirements of both the program and the student's department of concentration. Submission is due on the same date as your department deadline for thesis or junior independent work. All work will be reviewed by the Statistics and Machine Learning certificate committee. At the end of academic each year, there will be a public poster session for students to present their work to each other, to other students, and to the faculty.
Finally, students are encouraged to attend one of the Statistics and Machine Learning colloquia on campus. These include the Wilks Statistics Seminar, the Machine Learning Seminar, the Political Methodology Seminar, and the Quantitative and Computational Biology Seminar.
One of the following courses (Foundations of Statistics)
ECO 202 Statistics and Data Analysis for Economics
EEB 355 Introduction to Statistics for Biology (also MOL 355)
ORF 245 Fundamentals of Engineering Statistics
POL 345 Quantitative Analysis and Politics
PSY 251 Quantitative Methods
One of the following courses (Foundations of Machine Learning)
COS 424 Interacting with Data
ORF 350 Analysis of Big Data
Three of the following courses (including those above, with permission)
COS 402 Artificial Intelligence
ORF 418 Optimal Learning
MAT 385 Probability Theory
ORF 309 Probability and Stochastic Systems
ORF 473 Special Topics in Operations Research and Financial Engineering: Stochastic calculus
ECO 302 Econometrics
ECO 312 Econometrics: A Mathematical Approach
ECO 313 Econometric Applications
ELE 486 Transmission and Compression of Information
GEO 422 Data, Models, and Uncertainty in the Natural Sciences
MOL 436 Statistical Methods for Genomic Data
ORF 405 Regression and Time Series
POL 346 Applied Quantitative Analysis
Students who fulfill the program requirements receive a certificate upon graduation.