Undergraduate Certificate Program

Graph of personality types by occupation
Example of work from Vinicius Wagner '21 and Hari Raval '21 from the course SML 201. The image displays data visualization that tracks personality types to occupation.

Overview

The Undergraduate Certificate 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 cellular-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 also leave them equipped to 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 and submitted an application, 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 certificate’s independent work requirement, and attend CSML's 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 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 other students, researchers 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

Computer Science/CSML
(On Sabbatical for AY21-22)

Peter Melchior
Acting Director, Undergraduate Certificate

Astrophysics/CSML

Prateek Mittal

ECE

John Mulvey

ORFE

Peter J Ramadge

ECE/CSML

Marc Ratkovic

Politics

Mengdi Wang

ECE/CSML

Certificate Associated Faculty

Yacine Ait-Sahalia

Economics

Amir Ali Ahmadi

ORFE

Sanjeev Arora

Computer Science

Matias Cattaneo
ORFE
Danqi Chen

Computer Science

Jonathan Cohen

Psychology/PNI

Jia Deng

Computer Science

Adji Bousso Dieng

COS

Barbara Engelhardt

Computer Science

Jaime Fernández Fisac

ECE

Filiz Garip

Sociology

Tom Griffiths

Psychology

Boris Hanin

ORFE

Elad Hazan

Computer Science

Bo Honoré

Economics

Daisy Huang

CSML

Niraj Jha

ECE

Chi Jin

ECE

Jason Klusowski

ORFE

Michal Kolesár

Economics

S.Y. Kung

ECE

Ching-Yao Lai

Geosciences

Jason Lee

ECE

Naomi Leonard

MAE

Mariangela Lisanti

Physics

John Londregan

Politics/SPIA

Anirudha Majumdar

MAE

Meredith Martin

English/CDH

Ricardo Masini

CSML

William Massey

ORFE

Reed Maxwell

High Meadows Environmental Institute/CEE

Peter Melchior
Acting Director, Undergraduate Certificate

Astrophysics/CSML

Ulrich Müller

Economics

Karthik Narasimhan

Computer Science

Kenneth Norman

Psychology/PNI

Jonathan Pillow

Psychology/PNI

Mikkel Plagborg-Moller

Economics

H. Vincent Poor

ECE

Yuri Pritykin

Genomics

Miklos Racz

ORFE

Ben Raphael

Computer Science

Olga Russakovsky

Computer Science

Matthew Salganik

Sociology

H. Sebastian Seung

CS/PNI

Amit Singer

Mathematics/PACM

Mona Singh

CS/Genomics

Bartolomeo Stellato

ORFE

Brandon Stewart

Sociology

John D. Storey

Genomics

Michael Strauss

Astrophysical Sciences

Rocío Titiunik
Politics
Jeroen Tromp

Geosciences

Olga Troyanskaya

CS/Genomics

Robert Vanderbei

ORFE

Mark Watson

Economics

Yu Xie

Sociology/PIIRS