SML Graduate Certificate Course Table

The course trees shown below are designed to help you understand the range of Princeton courses offered in Data Sciences, Machine Learning, Optimization, Probability and Statistics, and to assist students and faculty advisors in planning course selections.  All of the courses listed are full semester, graded courses.

Please note that courses may not be offered every semester nor every year. In addition, some courses have limited enrollment and fill up quickly. To check if a course is being courses offered in a particular semester, and its enrollment cap, please check the Registrar’s website, or the website of the home department. 

If you have any questions or see any errors or omissions, please send us an email.  


SML Graduate Certificate Course Table

Machine Learning Core Courses (at least one course from the list below)
COS 511: Theoretical Machine Learning
COS 524: Foundations of Machine Learning
ECE 535: Machine Learning & Pattern Recognition (P)

Statistics Core Courses (at least one course from the list below)
COS/SML 513: Foundation of Probabilistic Modeling (P)
ECO 513: Advanced Econometrics: Time Series Models
ECO 519: Advanced  Econometrics: Nonlinear Models
ORF 524: Statistical Theory and Methods
POL 572: Quantitative Analysis II
SML/AST 505: Modern Statistics

Electives (one from the list below, or an additional course from the core lists above, with permission.)

Machine Learning
ECE 524: Foundations of Reinforcement Learning
ECE 538b: Theory of Weakly Supervised Deep Learning
ECE 539: Optimization for Machine Learning
ORF 543: Deep Learning Theory
COS 534: Fairness in Machine Learning

Data Science and Statistics
NEU 545: Statistics for Neuroscience
ORF 525: Statistical Foundations of Data Science

ECE 525: Random Processes in Information Systems
ORF 522: Linear and Nonlinear Optimization
ORF 523: Convex and Conic Optimization
ORF 544: Stochastic Optimization

CEE 505: Prob.& Statistics for Civil Engineering
COS 529: Advanced Computer Vision
COS 584: Advanced Natural Language Processing
ECE 522: Large Scale Optimization for Data Science
ECE 530: Theory of Detection and Estimation
ECO 515: Applied Econometrics (R)
NEU 560: Statistical Modeling & Analysis of Neural Data
ORF 505: Statistical Analysis of Financial Data
POL 573: Quantitative Analysis III
POL 574: Quantitative Analysis IIII
SOC 504: Advanced Social Studies

NOTES (Graduate Certificate)


  1. Courses may have distinct prerequisites, and not all courses are offered every year. Please check the registrar’s website.

Machine Learning

  1. COS 524, ECE 535, and COS 511 are sufficiently distinct to be considered complementary courses.
  2. COS 524 vs ECE 535. Both courses cover ML algorithms, but differ in focus and many (but not all) of the specific topics. COS 524 focuses on the problem of analyzing large complex data sets including text, images, and biological data and has a major data analysis final project. ECE 535 is a proof based course focusing on the theoretical underpinnings and properties of ML algorithms. It has regular theory problem sets, programming assignments (Python), a midterm, and a final exam. 
  3. ECE 436 Machine Learning Theory (UG course tables) covers algorithmic independent bounds on ML. It is complementary to both COS 524 and ECE 535.


  1. The courses COS/SML 513 and SML/AST 505 are related but differ in several ways. Both courses use Bayesian approaches, emphasize fundamental concepts, and end with a final project in lieu of an exam. COS/SML 513 covers probabilistic model specification and evaluation and efficient computational algorithms for prediction and decision making. SML/AST 505 introduces a statistical approach to data and leverages probabilistic programming frameworks for quickly performing analyses in applications across various scientific disciplines. 
  2. ORF 524 is a broad introduction to modern statistical methods from a theoretical perspective. The course covers statistical theory and methods for point estimation, confidence intervals (including modern bootstrapping), and hypothesis testing. It has homeworks, a midterm, and a final exam. It is complementary to COS/SML 513 and SML/AST 505.
  3. POL 572 covers applied regression analysis in cross-section settings. It introduces the basic principles of causal inference and then covers various statistical techniques including linear regression and instrumental variables. It offers a more focused perspective on the use of modern statistics for the study of social data.