Program in Statistics and Machine Learning
- Ryan P. Adams
- Ryan P. Adams, Computer Science
- Prateek Mittal, Electrical Engineering
- John M. Mulvey, Oper Res and Financial Eng
- Peter J. Ramadge, Electrical Engineering
- Marc Ratkovic, Politics
- Mengdi Wang, Electrical Engineering
- Emmanuel A. Abbe, Electrical Engineering
- Amir Ali Ahmadi, Oper Res and Financial Eng
- Sanjeev Arora, Computer Science
- Yacine Aït-Sahalia, Economics
- Matias D. Cattaneo, Oper Res and Financial Eng
- Danqi Chen, Computer Science
- Yuxin Chen, Electrical Engineering
- Jonathan D. Cohen, Psychology
- Jia Deng, Computer Science
- Abigail G. Doyle, Chemistry
- Barbara E Engelhardt, Computer Science
- Jianqing Fan, Oper Res and Financial Eng
- Tom Griffiths, Psychology
- Elad Hazan, Computer Science
- Bo E. Honoré, Economics
- Niraj K. Jha, Electrical Engineering
- Chi Jin, Electrical Engineering
- Michal Kolesár, Economics
- Sun-Yuan Kung, Electrical Engineering
- Jason D. Lee, Electrical Engineering
- Naomi E. Leonard, Mechanical & Aerospace Eng
- Mariangela Lisanti, Physics
- John B. Londregan, Schl of Public & Int'l Affairs
- Anirudha Majumdar, Mechanical & Aerospace Eng
- Meredith A. Martin, English
- William A. Massey, Oper Res and Financial Eng
- Peter M. Melchior, Astrophysical Sciences
- Ulrich K. Mueller, Economics
- Karthik Narasimhan, Computer Science
- Kenneth A. Norman, Psychology
- Jonathan W. Pillow, Psychology
- Mikkel Plagborg-Moller, Economics
- H. Vincent Poor, Electrical Engineering
- Ben Raphael, Computer Science
- Olga Russakovsky, Computer Science
- Matthew J. Salganik, Sociology
- H. Sebastian Seung, Computer Science
- Christopher A. Sims, Economics
- Amit Singer, Mathematics
- Mona Singh, Computer Science
- Brandon M. Stewart, Sociology
- John D. Storey, Integrative Genomics
- Michael A. Strauss, Astrophysical Sciences
- Rocío Titiunik, Politics
- Jeroen Tromp, Geosciences
- Olga G. Troyanskaya, Computer Science
- Robert J. Vanderbei, Oper Res and Financial Eng
- Mark W. Watson, Schl of Public & Int'l Affairs
- Yu Xie, Sociology
Sits with Committee
- Michael Guerzhoy
- Daisy Yan Huang
Information and Departmental Plan of Study
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.
Admission 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 and independent work (as outlined below), as well as any prerequisites for the selected courses.
For enrollment, please use this form: Certificate Enrollment Application(link is external)
For questions, contact us at email@example.com
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.
- 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).
- One machine learning course from the approved list. Student must receive at least a B- (pdf not permitted).
- 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.
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.