Graduate Certificate Requirements

Current Princeton students already enrolled in a Ph.D.  or Masters program are eligible to enroll for this certificate.

The graduate certificate is comprised of three components: (a) completion of three appropriate graduate courses, (b) a relevant research contribution, and (c) a research seminar.  More details on each of these are below.  If you have additional questions, please contact csmlgrad@princeton.edu

Coursework

Students are required to take a total of three courses from approved lists and earn at least a B+ for each course: one Core Machine Learning, one Core Statistics and Probabilistic Modeling, and one Elective.  With the permission of the program director, the elective course can be selected from a core category provided it does not significantly overlap with the other course selected from that category. At least one of the three courses must be outside the student's home department and at most one course can be below the 500 level.  Students may not count courses that are used to satisfy core requirements in their home department concentration toward this certificate, however they may count up to two electives that were taken for their degree requirements.

Research Component

To ensure that an important component of the student's thesis involves either rigorous data analysis, and/or mathematical or computational modeling of data or machine learning problems, one of the thesis or research paper readers must be a participating graduate certificate faculty member. This reader will be required to either send a letter, or their reader's report, to the program director to verify that the research satisfies this requirement.

Graduate Research Seminar

The CSML graduate seminar course serves as a venue for reporting current results and discussing the integration of different research approaches to data analysis. Enrollment, attendance and participation in the CSML graduate seminar for at least one semester helps teach students how to communicate their research to a broad audience, and encourages the development of skills for interacting with other students, postdoctoral fellows, and faculty who are investigating data analysis problems. It also serves to build a supportive community of young scholars with shared interests.


Courses

Core Machine Learning – one of the following courses
  • COS 402 Machine Learning and Artificial Intelligence
  • ELE 535 Machine Learning and Pattern Recognition
  • COS 424 Fundamentals of Machine Learning
  • COS 485 Neural Networks: Theory and Applications
  • COS 511 Theoretical Machine Learning
Core Statistics and Probabilistic Modeling – one of the following courses
  • ECO 513 Time Series Econometrics
  • ECO 519 Advanced Econometrics: Nonlinear Models
  • ORF 524 Statistical Theory and Methods
  • COS 513 Foundations of Probabilistic Modeling
  • ELE 530 Estimation and Detection
  • POL 572 Quantitative Analysis II
  • QCB 508 Foundations of Applied Statistics and Data Science
Electives – one of the following courses (including those above, with permission)
  • APC 527 Random Graphs and Networks
  • APC/ORF 550 - Topics in Probability - Probability in High Dimension
  • COS 534 - Fairness in Machine Learning
  • ELE 477 Kernel-Based Machine Learning
  • ORF 505 Statistical Analysis of Financial Data
  • POL 573 Quantitative Analysis III
  • POP 507 Generalized Linear Statistical Models
  • ORF 522 Linear and Nonlinear Optimization
  • ECO 515 Econometric Modeling
  • ELE 538B Sparsity, Structure, and Inference
  • ELE 538C Large-Scale Optimization in Data Science
  • MAT585/APC520 Mathematical Analysis of Massive Data Sets
  • ORF 523 Convex and Conic Optimization
  • ORF 525 Statistical Learning & Nonparametric Estimation
  • POL 574 Quantitative Analysis IV
  • SOC 504 Advanced Social Statistics
  • NEU 560 - Statistical Modeling and Analysis of Neural Data