Program Overview

Graduate Certificate Program

The Graduate Certificate in Statistics and Machine Learning is open to all Princeton University students enrolled in a master’s or Ph.D. program. This program fosters and deepens students’ understandings of data science and may complement a wide variety of degree programs across campus. The certificate is designed to formalize the training of students who apply data, statistics, and machine learning principles to their research.

Program Components

The SML graduate certificate program is defined by three components:

1. Appropriate coursework
2. Relevant research contribution
3. The CSML graduate seminar

Enrollment Details

This certificate program is open to Princeton University students currently enrolled in a Ph.D. or master’s program. Students must enroll by completing an online application form. Students are encouraged to sign up as soon as possible, and no later than one semester prior to graduation. Ph.D. students must enroll in all courses prior to entering Dissertation Completion Enrollment (DCE) status.

Optional Preparatory Courses

Machine Learning

• ELE 364 Machine Learning for Predictive Data Analytics
• COS 324 Introduction to Machine Learning
• MAT 390 Mathematical Introduction to Machine Learning
• MAE 345 Robotics and Intelligent Systems
• The CSML Python Workshop

Statistics and Probabilistic Modeling

• ORF 309 Probability and Random Processes
• POL 346 Applied Quantitative Analysis
• ORF 350 Analysis of Big Data
• SOC 500 Applied Social Statistics
• ECO 517, 518 Econometric Theory I, II
• ELE 525 Random Processes in Information Systems
• POL 571 Quantitative Analysis I
• The CSML R Workshop

Requirements

Requirements

Students must complete three courses from the approved course list, maintaining an average GPA of B+ (3.3) or better. The list includes three categories: core machine learning, core statistics and probabilistic modeling, and electives. One course must be selected from each category. With permission from the certificate director, the elective course may be chosen from a core category, provided it does not significantly overlap with another course selected from that category. At least one of the three courses must be outside the student’s home department, and no more than one course can be below the 500 level.

Research Component

For students completing a thesis or dissertation as part of their degree, the thesis, dissertation, or a publishable research paper must include a significant component that contributes to statistics or machine learning or rigorously applies such methods in a specific domain. For non-thesis master’s degree students, this requirement can be met through a technical presentation on a topic relevant to the program.

Graduate Research Seminar

Prior to graduation and before entering DCE status, students must enroll in and complete the requirements of the CSML graduate seminar series (SML 510) for at least one semester. SML 510 is exclusively for CSML graduate certificate students and is a mandatory course for the program.

Approved Course Lists

Core Machine Learning – Select One

• COS 485 Neural Networks: Theory and Applications
• COS 511 Theoretical Machine Learning
• ECE 535 Machine Learning and Pattern Recognition

Core Statistics and Probabilistic Modeling - Select One

• COS 513 Foundations of Probabilistic Modeling
• ECO 513 Advanced Econometrics: Time Series Models
• ECO 519 Advanced Econometrics: Nonlinear Models
• ORF 524 Statistical Theory and Methods
• POL 572 Quantitative Analysis I
• QCB 508 Foundations of Statistical Genomics
• SML 505 Modern Statistics

Electives – Select One

• APC/ORF 550 Topics in Probability: Probability in High Dimension
• COS 534 Fairness in Machine Learning
• ECO 515 Econometric Modeling
• FIN 580 Quantitative Data Analysis in Finance
• NEU 560 Statistical Modeling and Analysis of Neural Data
• ORF 505 Statistical Analysis of Financial Data
• ORF 522 Linear and Nonlinear Optimization
• ORF 523 Convex and Conic Optimization
• ORF 525 Statistical Foundations of Data Science
• ORF 526 Probability Theory
• POL 573 Quantitative Analysis II
• POL 574 Quantitative Analysis III
• SOC 504 Advanced Social Statistics

SML Graduate Courses

SML 510 – Graduate Research Seminar

This course is a semester-long seminar series designed for graduate students enrolled in the Graduate Certificate Program in Statistics and Machine Learning. It provides a platform for students to present and discuss research involving statistics and machine learning in a supportive, peer-driven environment.

SML 505 – Modern Statistics

This course offers an introduction to modern statistics and data analysis, focusing on the question, “What should I do if this is my data, and this is what I want to know?”

SML 543 – Machine Learning: A practical introduction for humanists and social scientists

Machine learning – and in particular, deep learning – is rapidly opening new horizons for research in the humanities and social sciences. This course offers a practical introduction to deep learning for graduate students, without assuming knowledge of calculus or other college-level math, or any prior experience with coding.

TA Positions at CSML

The Center for Statistics and Machine Learning at Princeton University has openings for TAs to teach precepts for SML courses being taught in the Spring 2026 semester.

SML 201 - Introduction to Data Science

TAs are required to lead an 80-minute precept for the entire semester and to attend two weekly course lectures.

SML 301 - Data Intelligence: Modern Data Science Methods

The TA will assist with designing assignments, hold office hours, assist with grading, hold Python workshops for students who are beginners in Python, and lead precept sessions.

SML 312 - Research Projects in Data Science

The TA will work with students who are working on research projects that fall within the TAs specialization, hold office hours, assist with grading, hold Python workshops for students who are beginners in Python, and lead precept sessions.

FAQ

• Do I need to formally enroll in the certificate program?
  Yes, please use the enrollment form to enroll in the program.
• Can I enroll in the certificate program if I am not a Princeton University student?
  No, enrollment is limited to Princeton University students.
• Can I use core courses from my home department to fulfill the three-course requirement?
  No, only designated courses within the program count toward the requirement.
• What if I don’t have an advisor or reader affiliated with the CSML?
  The program director can sign off on your paper.
• Can anyone take SML 510 (Seminar Course)?
  No, SML 510 is only open to students in the SML certificate program.
• How do I enroll in SML 510?
  Please complete the enrollment form for SML 510.
• Can I take SML 510 while I am in DCE status?
  No, this is an active course that must be completed before entering DCE status.
• I am unable to enroll in SML 510. Is there something special I need to do?
  Yes, you will need special permission to enroll, which the academic program manager will provide.
• Can I use courses not on the official course list?
  To request consideration for courses not on the approved list, please complete the Graduate Course Exception Form.
• Can I wait until my last semester to take SML 510?
  SML 510 may not be offered every semester, as availability depends on class enrollment.