Students are required to take a total of five courses and earn at least B- for each course: one of the “Foundations of Statistics” courses, one of the “Foundations of Machine Learning” courses, and three elective courses. With all necessary permissions, advanced students may also take approved graduate-level courses. Students may count at most two courses from another degree program (departmental concentration) towards this certificate program.

Students are also 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 program and the student's department of concentration. Submission is due on the same date as your department deadline for thesis or junior independent work. All work will be reviewed by the Statistics and Machine Learning Certificate committee. At the end of each year, there will be a public poster session at which students are required to present their work to each other, to other students, and to the faculty.

Finally, students are encouraged to attend one of the Statistics and Machine Learning colloquia on campus, including the CSML Seminar Series.

## 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 smlcert@princeton.edu

## Courses

##### Foundations of Statistics - one of the following courses

- ECO 202 Statistics & Data Analysis for Economics
- ORF 245 Fundamentals of Statistics
- POL 345/SOC 305 Intro to Quantitative Social Science
- PSY 251 Quantitative Methods
- WWS 200 Statistics for Social Science

##### Foundations of Machine Learning - one of the following courses

- COS 324 Introduction to Machine Learning
- COS 424/SML 302 Fundamentals of Machine Learning
- ECE 364 Machine Learning for Predictive Data Analytics
- ECE 435 Machine Learning and Pattern Recognition
- MAT 490 Mathematical Introduction to Machine Learning
- ORF 350 Analysis of Big Data

## Electives

Three of the following courses (including those above, with permission)

##### Data Science

- POL 245 Visualizing Data
- SML 201 Introduction to Data Science
- SML 310 Research Projects in Data Science

##### Machine Learning

- COS 402 Machine Learning and Artificial Intelligence
- COS 429 Computer Vision
- COS 484 Natural Language Processing
- COS 485 Neural Networks –Theory and Applications
- ECE 477 Kernel-Based Machine Learning
- ECE 488 Image Processing
- ORF 418 Optimal Learning

##### Theory

- MAT 385 Probability Theory
- ORF 309 Probability and Stochastic Systems
- ORF 363 Computing and Optimization for the Physical and Social Sciences

##### Applications

- AST 303 Modeling and Observing the Universe: Research Methods in Astrophysics
- CEE 460 Risk Analysis
- ECO 302 Econometrics
- ECO 312 Econometrics: A Mathematical Approach
- ECO 313 Econometric Applications
- ECE 382 Probabilistic Systems and Information Processing
- ECE 480/NEU 480/PSY 480 fMRI Decoding: Reading Minds Using Brain Scans
- GEO 422 Data, Models, and Uncertainty in the Natural Sciences
- MAE 345 Robotics and Intelligent Systems
- ORF 405 Regression and Applied Time Series
- ORF 473 FinTech: Data Driven Innovation
- POL 346 Applied Quantitative Analysis
- PSY 454/COS 454 Probabilistic Models of Cognition
- QCB 408 Foundations of Applied Statistics and Data Science
- QCB 455/551 Introduction to Genomics and Computational Molecular Biology
- SOC 400 Applied Social Statistics
- SOC 412 Designing Social and Behavioral Experiments at Scale

** COS 302/SML 305 - Mathematics for Numerical Computing and Machine Learning** is a new course that provides background for students interested in mathematics for computer science. It is a preparatory course for machine learning but it is not a core or elective course for the SML certificate.

### Example Paths for the SML Certificate

##### Computer Science, Mathematics, or Engineering Student

- ORF 245 Fundamentals of Engineering Statistics
- COS 324 Introduction to Machine Learning
**OR**ORF 350 Analysis of Big Data

- ORF 309 Probability and Stochastic Systems
- ELE 435 Machine Learning and Pattern Recognition
- COS 485 Neural Networks: Theory and Applications

##### Economics or Finance Student

- ORF 245 Fundamentals of Engineering Statistics
- ORF 350 Analysis of Big Data
- ECO 312 Econometrics: A Mathematical Approach
- ECO 313 Econometrics Applications
- COS 424 Fundamentals of Machine Learning
**OR**ECE 435 Machine Learning and Pattern Recognition

##### Life Sciences Student

- PSY 251 Quantitative Methods
- ECE 364 Machine Learning for Predictive Data Analytics
- SML 201 Introduction to Data Science
- SML 310 Research Projects in Data Science
**OR**GEO 422 Data, Models, and Uncertainty in the Natural Sciences

- QCB 408 Foundations of Applied Statistics and Data Science

##### Social Sciences Student

- POL 345 Quantitative Analysis and Politics
- ECE 364 Machine Learning for Predictive Data Analytics
**OR**COS 424 Fundamentals of Machine Learning

- ECO 312 Econometrics: A Mathematical Approach
- ECO 313 Econometric Applications
- POL 346 Applied Quantitative Analysis

Please note that not all courses are offered in all years; students are encouraged to consult with the relevant departments with questions about particular courses. If you find a course in our list is no longer offered, please bring it to out attention by emailing smlcert@princeton.edu. Thank you!