Undergraduate Certificate Program

Overview

The Program in Statistics and Machine Learning is designed for students, majoring 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 a variety of disciplines to address 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. The 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.

Enrollment to the Program

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, independent work (as outlined below), and any prerequisites for the selected courses.

For enrollment, please use this form: Certificate Enrollment Application

For questions, contact us at smlcert@princeton.edu

Program of Study

Students are required to take a total of five courses and earn at least 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 another degree program (departmental concentration) towards this certificate program.

Students are also required to complete a thesis or at least one semester of independent work in their junior or senior year on a topic that makes substantial application or study of machine learning or statistics. In situations where this is not feasible, students should consult with the program director to discuss alternate arrangements.  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 each year, there will be a public poster session at which students are required 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, including the CSML sponsored or co-sponsored seminars.

Certificate of Proficiency

Students who fulfill the program requirements receive a certificate upon graduation.

Certificate Program Faculty

Director of the Certificate Program:

TBA

Executive Committee:

Barbara Engelhardt, Jianqing Fan, Peter Ramadge

Associated Faculty:

Emmanuel AbbeYacine Ait-Sahalia, Amir Ali Ahmadi, Sanjeev AroraJonathan CohenYuxin ChenDavid Dobkin, Kirill Evdokimov, Elad Hazan, Bo Honore, Kosuke ImaiMichal Kolesar, Samory Kpotufe, Sanjeev KulkarniUlrich Mueller,  Jonathan Pillow, Peter Ramadge, Marc Ratkovic, Matthew Salganik, H. Sebastian Seung, Christopher Sims, Amit Singer, Mona Singh, Michael Strauss, Brandon StewartJohn D. StoreyOlga Troyanskaya, Ramon Van Handel, Robert Vanderbei, Sergio Verdu, Mark Watson, Yu Xie

Associated Lecturers:

German Rodriguez