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 [email protected]

 

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 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. 

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 the required course page.

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/Center for Statistics & Machine Learning
Christine Allen-Blanchette
Mechanical & Aerospace Engineering/Center for Statistics & Machine Learning
Peter Melchior
Astrophysics/Center for Statistics & Machine Learning
Brandon Stewart
Sociology
Mengdi Wang
Electrical & Computer Engineering/Center for Statistics & Machine Learning

Certificate Associated Faculty

Sigrid Adriaenssens
Civil & Environmental Engineering
Yacine Ait-Sahalia
Economics
Amir Ali Ahmadi
Operations Research & Financial Engineering
Christine Allen-Blanchette
Mechanical & Aerospace Engineering/Center for Statistics & Machine Learning
Sanjeev Arora
Computer Science
Matias Cattaneo
Operations Research & Financial Engineering
Danqi Chen
Computer Science
Jonathan Cohen
Psychology/Princeton Neuroscience Institute
Jia Deng
Computer Science
Adji Bousso Dieng
Computer Science
Jaime Fernández Fisac
Electrical & Computer Engineering
Filiz Garip
Sociology
Tom Griffiths
Psychology
Boris Hanin
Operations Research & Financial Engineering
Elad Hazan
Computer Science
Bo Honoré
Economics
Niraj Jha
Electrical & Computer Engineering
Chi Jin
Electrical & Computer Engineering
Jason Klusowski
Operations Research & Financial Engineering
Michal Kolesár
Economics
S.Y. Kung
Electrical & Computer Engineering
Ching-Yao Lai
Geosciences
Jason Lee
Electrical & Computer Engineering
Naomi Leonard
Mechanical & Aerospace Engineering
Sarah Jane Leslie
Philosophy
Mariangela Lisanti
Physics
John Londregan
Politics/Princeton School of Public & International Affairs
Anirudha Majumdar
Mechanical & Aerospace Engineering
Meredith Martin
English/Center for Digital Humanities
William Massey
Operations Research & Financial Engineering
Reed Maxwell
High Meadows Environmental Institute/Civil & Environmental Engineering
Peter Melchior
Astrophysics/Center for Statistics & Machine Learning
Ulrich Müller
Economics
Karthik Narasimhan
Computer Science
Kenneth Norman
Psychology/Princeton Neuroscience Institute
Jonathan Pillow
Psychology/Princeton Neuroscience Institute
Mikkel Plagborg-Moller
Economics
H. Vincent Poor
Electrical & Computer Engineering
Yuri Pritykin
Genomics
Miklos Racz
Operations Research & Financial Engineering
Ben Raphael
Computer Science
Olga Russakovsky

Computer Science

Matthew Salganik
Sociology
H. Sebastian Seung
Computer Science/Princeton Neuroscience Institute
Amit Singer
Mathematics/Program in Applied & Computational Mathematics
Mona Singh
Computer Science/Genomics
Bartolomeo Stellato
Operations Research & Financial Engineering
Brandon Stewart
Sociology
John D. Storey
Genomics
Michael Strauss
Astrophysical Sciences
Rocío Titiunik
Politics
Jeroen Tromp
Geosciences
Olga Troyanskaya
Computer Science/Genomics
Robert Vanderbei
Operations Research & Financial Engineering
Mark Watson
Economics
Michael Webb
Chemicanl & Biological Engineering
Yu Xie
Sociology/Princeton Institute for International & Regional Studies