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 a B-, complete the independent work requirement, and attend the annual poster session. 

  1. Course Work:
    1. One statistics course from the approved list. Student must receive at least a B- (pdf is not permitted.  Credit or exemptions for AP exams is not permitted).
    2. One machine learning course from the approved list. Student must receive at least a B- (pdf not permitted).
    3. Three electives from the approved list.  Student must receive at least a B- (pdf not permitted).

Students may count at most two courses from their departmental concentration or other certificate program toward the certificate. With permission, advanced students may be permitted to take approved graduate-level courses.

  1. Independent Work and SML Poster Session: Students are 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.  This work may be used to satisfy the requirements of both the SML certificate program and the student's department of concentration. All work will be reviewed by the Statistics and Machine Learning Certificate committee. In May, 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. Students must adhere to submission due dates for independent work papers and poster requirements. Attendance for the poster session is mandatory

Finally, students are encouraged to attend one of the Statistics and Machine Learning colloquia on campus, including the CSML sponsored or co-sponsored seminars.

For a list of required courses that will count towards the certificate, please visit our website (link is external).  

Certificate of Proficiency

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

Certificate Executive Committee

  • Ryan Adams

      • Director, Undergraduate Certificate

    Computer Science

  • Prateek Mittal

    Electrical Engineering

  • John Mulvey

    ORFE

  • Peter J Ramadge

    EE/CSML

  • Marc Ratkovic

    Politics

  • Mengdi Wang

    ORFE

Certificate Associated Faculty

  • Emmanuel Abbe

    Electrical Engineering

  • Yacine Ait-Sahalia

    Economics

  • Amir Ali Ahmadi

    ORFE

  • Sanjeev Arora

    Computer Science

  • Yuxin Chen

    Electrical Engineering

  • Jonathan Cohen

    Psychology/PNI

  • Nick Feamster

    CS/CITP

  • Elad Hazan

    Computer Science

    *on leave Spring and Fall 2018*

  • Bo Honoré

    Economics

  • Daisy Huang

    CSML

  • Michal Kolesár

    Economics

  • Naomi Leonard

    MAE

  • John Londregan

    Politics/WWS

  • Anirudha Majumdar

    MAE

  • Meredith Martin

    English/CDH

  • William Massey

    ORFE

  • Prateek Mittal

    Electrical Engineering

  • Ulrich Müller

    Economics

  • John Mulvey

    ORFE

  • Arvind Narayanan

    Computer Science

  • Kenneth Norman

    Psychology/PNI

  • Jonathan Pillow

    Psychology/PNI

  • H. Vincent Poor

    Electrical Engineering

  • Warren Powell

    ORFE

  • Marc Ratkovic

    Politics

  • German Rodriguez

    Population Research

  • Olga Russakovsky

    Computer Science

  • Matthew Salganik

    Sociology

  • H. Sebastian Seung

    CS/PNI

  • Christopher Sims

    Economics

  • Yoram Singer

    Computer Science

  • Amit Singer

    Mathematics/PACM

  • Mona Singh

    CS/Genomics

  • Brandon Stewart

    Sociology

  • John D. Storey

    Genomics

  • Michael Strauss

    Astrophysical Sciences

  • Olga Troyanskaya

    CS/Genomics

  • Ramon van Handel

    ORFE/PACM

  • Robert Vanderbei

    ORFE

  • Mengdi Wang

    ORFE

  • Mark Watson

    Economics

  • Yu Xie

    Sociology/PIIRS